diff --git a/examples/references.bib b/examples/references.bib
index e5a8f246c..8222dc48b 100644
--- a/examples/references.bib
+++ b/examples/references.bib
@@ -1,3 +1,13 @@
+@article{andersen1997intraday,
+  author        = {Andersen, Torben G. and Bollerslev, Tim},
+  doi           = {10.1016/s0927-5398(97)00004-2},
+  journal       = {Journal of Empirical Finance},
+  number        = {2-3},
+  pages         = {115--158},
+  title         = {Intraday periodicity and volatility persistence in financial markets},
+  volume        = {4},
+  year          = {1997}
+}
 @article{ando2007bayesian,
   title         = {Bayesian predictive information criterion for the evaluation of hierarchical Bayesian and empirical Bayes models},
   author        = {Ando, Tomohiro},
@@ -94,6 +104,16 @@ @misc{betancourt2018
   primaryclass  = {stat.ME},
   url           = {https://arxiv.org/abs/1701.02434}
 }
+@article{bollerslev1987conditionally,
+  author        = {Bollerslev, Tim},
+  doi           = {10.2307/1925546},
+  journal       = {The Review of Economics and Statistics},
+  number        = {3},
+  pages         = {542--547},
+  title         = {A conditionally heteroskedastic time series model for speculative prices and rates of return},
+  volume        = {69},
+  year          = {1987}
+}
 @article{bonilla2007multioutput,
   title         = {Multi-task Gaussian process prediction},
   author        = {Bonilla, Edwin V and Chai, Kian and Williams, Christopher},
@@ -269,6 +289,16 @@ @book{enders2022
   year          = {2022},
   publisher     = {The Guilford Press}
 }
+@article{engle1998autoregressive,
+  author        = {Engle, Robert F. and Russell, Jeffrey R.},
+  doi           = {10.2307/2999632},
+  journal       = {Econometrica},
+  number        = {5},
+  pages         = {1127--1162},
+  title         = {Autoregressive conditional duration: a new model for irregularly spaced transaction data},
+  volume        = {66},
+  year          = {1998}
+}
 @article{evans2006checking,
   title         = {Checking for prior-data conflict},
   author        = {Evans, Michael and Moshonov, Hadas},
@@ -303,7 +333,8 @@ @book{gelman2006data
   title         = {Data analysis using regression and multilevel/hierarchical models},
   author        = {Gelman, Andrew and Hill, Jennifer},
   year          = {2006},
-  publisher     = {Cambridge university press}
+  publisher     = {Cambridge University Press},
+  doi           = {10.1017/CBO9780511790942}
 }
 @article{gelman2006multilevel,
   title         = {Multilevel (hierarchical) modeling: what it can and cannot do},
@@ -417,6 +448,15 @@ @article{higgins2009meta
   year          = {2009},
   doi           = {10.1111/j.1467-985X.2008.00552.x}
 }
+@article{hoffman2013stochastic,
+  title         = {Stochastic Variational Inference},
+  author        = {Hoffman, Matthew D. and Blei, David M. and Wang, Chong and Paisley, John},
+  year          = {2013},
+  journal       = {Journal of Machine Learning Research},
+  volume        = {14},
+  pages         = {1303--1347},
+  url           = {https://jmlr.org/papers/v14/hoffman13a.html}
+}
 @article{hoffman2014nuts,
   title         = {The No-U-Turn Sampler: Adaptively Setting Path Lengths in Hamiltonian Monte Carlo},
   author        = {Hoffman, Matthew and Gelman, Andrew},
@@ -821,6 +861,16 @@ @unpublished{padonou2015polar
   month         = Feb,
   pdf           = {https://hal.archives-ouvertes.fr/hal-01119942v1/file/PolarGP_CircularDomains.pdf}
 }
+@article{page1954continuous,
+  author        = {Page, E. S.},
+  doi           = {10.1093/biomet/41.1-2.100},
+  journal       = {Biometrika},
+  number        = {1-2},
+  pages         = {100--115},
+  title         = {Continuous inspection schemes},
+  volume        = {41},
+  year          = {1954}
+}
 @book{pearl1985prob,
   title         = {Probabilistic Reasoning in Intelligent Systems: Networks of plausible Inference},
   author        = {Pearl, Judea},
@@ -871,6 +921,16 @@ @article{penrose1985
   year          = {1985},
   pages         = {189}
 }
+@article{polyak1992acceleration,
+  title         = {Acceleration of Stochastic Approximation by Averaging},
+  author        = {Polyak, Boris T. and Juditsky, Anatoli B.},
+  journal       = {SIAM Journal on Control and Optimization},
+  volume        = {30},
+  number        = {4},
+  pages         = {838--855},
+  year          = {1992},
+  doi           = {10.1137/0330046}
+}
 @misc{quiroga2022bart,
   title         = {Bayesian additive regression trees for probabilistic programming},
   author        = {Quiroga, Miriana and Garay, Pablo G and Alonso, Juan M. and Loyola, Juan Martin and Martin, Osvaldo A},
@@ -921,6 +981,16 @@ @online{rochford2018
   author        = {Austin Rochford},
   url           = {https://austinrochford.com/posts/2018-11-10-monotonic-predictors.html}
 }
+@article{roll1984simple,
+  author        = {Roll, Richard},
+  doi           = {10.1111/j.1540-6261.1984.tb03897.x},
+  journal       = {The Journal of Finance},
+  number        = {4},
+  pages         = {1127--1139},
+  title         = {A simple implicit measure of the effective bid-ask spread in an efficient market},
+  volume        = {39},
+  year          = {1984}
+}
 @book{rosenbaum2002observational,
   title         = {Observational Studies},
   author        = {Rosenbaum, Paul R.},
diff --git a/examples/variational_inference/streaming_tick_data.ipynb b/examples/variational_inference/streaming_tick_data.ipynb
new file mode 100644
index 000000000..c35c9fa12
--- /dev/null
+++ b/examples/variational_inference/streaming_tick_data.ipynb
@@ -0,0 +1,2843 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "fbd8fe51",
+   "metadata": {},
+   "source": [
+    "(streaming_tick_data)=\n",
+    "\n",
+    "# Streaming variational inference on high-frequency tick data\n",
+    "\n",
+    ":::{post} August 17, 2026\n",
+    ":tags: variational inference, minibatch, out-of-core, hierarchical model, time series\n",
+    ":category: advanced, tutorial\n",
+    ":author: Yicheng Yang\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "85392e8c",
+   "metadata": {},
+   "source": [
+    "Exchanges publish per-trade archives; Binance, for instance, distributes\n",
+    "aggregate trades as daily and monthly files at\n",
+    "[data.binance.vision](https://data.binance.vision). Widen a pull across\n",
+    "symbols and months and the feature matrix can outgrow the RAM of an ordinary\n",
+    "workstation; at that point in-memory minibatching stops being an option. PyMC's\n",
+    "{func}`~pymc.Minibatch` randomly slices tensor *inputs*; it is not itself a\n",
+    "disk-backed reader, so it cannot help once the array no longer fits. This\n",
+    "notebook fits a hierarchical hurdle–Student-t model of *next-event price\n",
+    "moves* by streaming minibatches from disk with pymc-extras'\n",
+    "[`DataLoader`](https://github.com/pymc-devs/pymc-extras/blob/8db1880d410e509be02abf9b085f08c3d4514fd1/pymc_extras/variational/dataloader.py),\n",
+    "on 300,000 synthetic\n",
+    "rows that are generated inside the notebook. It tries to teach three things:\n",
+    "\n",
+    "1. **When minibatch variational inference (VI) is even valid.** Two acceptance gates that most\n",
+    "   time-series models fail, and a model class that passes both.\n",
+    "2. **The mechanics, end to end** on an archive small enough to rebuild here:\n",
+    "   an on-disk global shuffle, streaming automatic differentiation variational\n",
+    "   inference (ADVI) with `total_size` rescaling, and\n",
+    "   what a stopping rule has to be able to see before it can fire.\n",
+    "3. **Checking the recovery.** With the truth known, recovery is checkable once\n",
+    "   one correction is made: the cyclic replay puts the optimizer on an orbit, and\n",
+    "   the last iterate misses the truth by several of its own posterior standard\n",
+    "   deviations purely because of where in that orbit the step budget ended.\n",
+    "   Averaged over one full pass, every identified row the recovery table reports\n",
+    "   lands inside 1.2 posterior standard deviations of its generating value,\n",
+    "   across three fits, one of them on a different replay order. Whether the\n",
+    "   widths themselves are calibrated is a separate question one dataset cannot\n",
+    "   settle; no claim about them is made here.\n",
+    "\n",
+    "The notebook times itself; the watermark at the end reports the wall time and the machine. Everything below executes on synthetic\n",
+    "data with known ground truth, which is what makes the recovery checks\n",
+    "possible: nothing here is a claim about any real market."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "7bc1b4b5",
+   "metadata": {},
+   "source": [
+    "## Two gates: when minibatch VI is valid, and when it is worth it\n",
+    "\n",
+    "Minibatch variational inference rests on one identity: if the likelihood\n",
+    "factors over rows given the parameters, then the batch log-likelihood scaled by\n",
+    "$N/b$ is an unbiased estimator of the full-data log-likelihood\n",
+    "{cite:p}`hoffman2013stochastic`. Two gates shaped the model in this notebook. The first is about *validity*;\n",
+    "the second is about whether streaming is doing any real work. Check both\n",
+    "before reaching for `total_size` on your own data:\n",
+    "\n",
+    "**Gate 1 — validity: the likelihood must factor over rows, and the batching\n",
+    "must weight every row equally.** No latent path coupling observations, no\n",
+    "label shared across rows, and every row given the same inclusion frequency by\n",
+    "the batching scheme (unequal inclusion breaks the plain $N/b$ rescaling). How\n",
+    "the batches are *drawn* is a separate matter that this gate does not settle:\n",
+    "fresh uniform batches make each step's scaled gradient conditionally unbiased\n",
+    "for the full-data objective (the identity above), while a fixed shuffled\n",
+    "order replayed every epoch visits each row once per pass, targets the same\n",
+    "finite sum, and yields cyclic rather than unbiased updates. This notebook does\n",
+    "the second, and returns to the distinction where the fit is described. The\n",
+    "factorization requirement is exactly why the classic\n",
+    "stochastic volatility model of the {ref}`stochastic_volatility` notebook\n",
+    "*cannot* be minibatched: its latent volatility path ties every observation to\n",
+    "its neighbors, so a random subset of rows does not carry $b/N$ of the\n",
+    "log-likelihood. Any model whose rows share one outcome (for example, every tick\n",
+    "of a match sharing the final result) fails the same gate through\n",
+    "pseudo-replication: the effective sample size is the number of outcomes, not\n",
+    "the number of rows.\n",
+    "\n",
+    "**Gate 2 — non-triviality: no low-dimensional sufficient statistics.** This\n",
+    "one is about whether the streaming machinery is needed at all, not about\n",
+    "validity: a Normal\n",
+    "likelihood with per-cell means and variances collapses to per-cell\n",
+    "$(\\sum y, \\sum y^2, n)$, so one linear scan computes the exact posterior\n",
+    "inputs and \"streaming inference\" degenerates into a glorified `groupby`:\n",
+    "valid, but theater. A hurdle alone does not rescue it; an observed Bernoulli\n",
+    "plus a Normal component still reduces to $(n_0, n_1, \\sum y, \\sum y^2)$. What\n",
+    "does break the collapse is the combination used below: a Student-t component\n",
+    "with an unknown $\\nu$, and row-level continuous covariates entering through\n",
+    "nonlinear links, so no fixed-dimensional summary of the rows suffices.\n",
+    "\n",
+    "The model below passes both gates. The case for each ingredient is that it is\n",
+    "the standard choice for the data feature it handles, not that it fits the\n",
+    "synthetic data, which was written to contain those features in the first\n",
+    "place."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ca2e69c5",
+   "metadata": {},
+   "source": [
+    ":::{include} ../extra_installs.md\n",
+    ":::\n",
+    "\n",
+    ":::{note}\n",
+    "The `DataLoader` merged into\n",
+    "pymc-extras after the v0.14.0 release, so it is not in a published version yet\n",
+    "and `pip install pymc-extras` will not provide it. The outputs stored in this\n",
+    "notebook were produced against the merge commit itself, which is also the\n",
+    "install line to use until the next release:\n",
+    "\n",
+    "```\n",
+    "pip install git+https://github.com/pymc-devs/pymc-extras@8db1880\n",
+    "```\n",
+    "\n",
+    "That pins pymc-extras `0.14.1.dev3+g8db1880d4`, and its own requirements pull\n",
+    "PyMC 6.2 and PyTensor 3.2, the versions reported by the watermark at the\n",
+    "bottom of this page. Once the module ships, `pip install pymc-extras` will do.\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "51568bd8",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:12:39.355958Z",
+     "iopub.status.busy": "2026-08-18T19:12:39.355880Z",
+     "iopub.status.idle": "2026-08-18T19:12:41.212539Z",
+     "shell.execute_reply": "2026-08-18T19:12:41.212031Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import gc\n",
+    "import logging\n",
+    "import os\n",
+    "import tempfile\n",
+    "import time\n",
+    "import warnings\n",
+    "\n",
+    "import arviz as az\n",
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import pyarrow as pa\n",
+    "import pyarrow.parquet as pq\n",
+    "import pymc as pm\n",
+    "import pytensor.tensor as pt\n",
+    "\n",
+    "from matplotlib.ticker import StrMethodFormatter\n",
+    "from pymc.blocking import DictToArrayBijection\n",
+    "from pymc_extras.variational.dataloader import DataLoader, parquet_source\n",
+    "from scipy import stats\n",
+    "\n",
+    "NOTEBOOK_T0 = time.perf_counter()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "d7d0dad0",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:12:41.214022Z",
+     "iopub.status.busy": "2026-08-18T19:12:41.213851Z",
+     "iopub.status.idle": "2026-08-18T19:12:41.217141Z",
+     "shell.execute_reply": "2026-08-18T19:12:41.216843Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%config InlineBackend.figure_format = 'retina'\n",
+    "RANDOM_SEED = 20260731\n",
+    "rng = np.random.default_rng(RANDOM_SEED)\n",
+    "az.style.use(\"arviz-variat\")\n",
+    "# the loss figures carry the fit story; keep the fit logger's lines out of the output\n",
+    "logging.getLogger(\"pymc\").setLevel(logging.ERROR)\n",
+    "# the predictive path compiles the custom `random` and the minibatch wrapper through\n",
+    "# numba, which falls back to object mode for both and says so; not actionable here\n",
+    "warnings.filterwarnings(\"ignore\", message=r\"Numba will use object mode\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9b1b9356",
+   "metadata": {},
+   "source": [
+    "## The model: hierarchical hurdle–Student-t next-event returns\n",
+    "\n",
+    "One row is one trade-to-trade transition $i$ on symbol $s$ at UTC hour $h$:\n",
+    "the return $y_i = 10^4 \\, (\\log p_{i+1} - \\log p_i)$ in basis points, and the\n",
+    "move indicator $m_i = \\mathbb{1}[y_i \\neq 0]$. Indexing by *events* rather than\n",
+    "by clock time has two consequences that matter before the algebra.\n",
+    "\n",
+    "First, prices move on a discrete grid, so an exact-zero return is not a\n",
+    "measure-zero event the way it is for a continuous variable: consecutive trades\n",
+    "can and do leave the price where it was. A purely continuous likelihood puts\n",
+    "zero probability mass on that outcome, whatever share of the rows it turns out\n",
+    "to occupy. The fix is a hurdle: model *whether* the price moves separately\n",
+    "from *how far* it moves given that it does.\n",
+    "\n",
+    "Second, each row is a transition between consecutive observed events, not a\n",
+    "fixed-duration return. So $1 - \\pi$ is the probability that the next event\n",
+    "leaves the price unchanged, and $\\sigma$ is the Student-t scale *conditional on\n",
+    "a move*. Neither is clock-time volatility: converting to a clock would\n",
+    "additionally require a model of event arrival times\n",
+    "{cite:p}`engle1998autoregressive`.\n",
+    "\n",
+    "$$\n",
+    "\\begin{aligned}\n",
+    "m_i &\\sim \\text{Bernoulli}(\\pi_i) \\\\\n",
+    "y_i \\mid m_i = 1 &\\sim \\text{StudentT}(\\nu,\\ \\mu_i,\\ \\sigma_i) \\\\\n",
+    "\\operatorname{logit} \\pi_i &= \\kappa_0 + b^{(\\kappa)}_s\n",
+    "  + B(h)^\\top\\!\\left(c + b^{(\\pi h)}_s\\right)\n",
+    "  + \\lambda_a a_i + \\lambda_q q_i \\\\\n",
+    "\\log \\sigma_i &= \\alpha_0 + b^{(\\alpha)}_s\n",
+    "  + B(h)^\\top\\!\\left(g + b^{(\\sigma h)}_s\\right)\n",
+    "  + (\\beta_a + b^{(\\beta a)}_s)\\, a_i + \\beta_q q_i \\\\\n",
+    "\\mu_i &= \\theta_d\\, d_i + \\theta_r\\, \\text{ylag}_i\n",
+    "\\end{aligned}\n",
+    "$$\n",
+    "\n",
+    "where\n",
+    "\n",
+    "* $\\kappa_0$ and $\\alpha_0$ are the global intercepts of the move probability\n",
+    "  and the log scale, and $b^{(\\kappa)}_s$, $b^{(\\alpha)}_s$ their per-symbol\n",
+    "  offsets;\n",
+    "* $B(h)$ is the hour-of-day Fourier basis (four columns), $c$ and $g$ its\n",
+    "  global coefficients, and $b^{(\\pi h)}_s$, $b^{(\\sigma h)}_s$ the per-symbol\n",
+    "  deviations from them;\n",
+    "* $\\lambda_a$, $\\lambda_q$, $\\beta_a$, $\\beta_q$ are the covariate slopes, with\n",
+    "  $b^{(\\beta a)}_s$ a per-symbol slope on activity in the scale;\n",
+    "* $\\theta_d$, $\\theta_r$ place the conditional location on the trade sign and\n",
+    "  the last nonzero return;\n",
+    "* every $b_s \\sim \\mathcal{N}(0, \\tau)$ with its own $\\tau$, and $\\nu$ is shared.\n",
+    "\n",
+    "The covariates are a simulated trade sign $d_i$, a notional-like $q_i$, an\n",
+    "activity-like $a_i$, and $\\text{ylag}_i$, the most recent previous nonzero\n",
+    "return on that symbol. In the generator below they are drawn directly rather\n",
+    "than engineered from a trade tape: only $\\text{ylag}$ is produced\n",
+    "sequentially, so it is the one covariate that could not see the future, and\n",
+    "$q$ and $a$ are standardized once over the whole sample. On a real archive\n",
+    "both would instead be trailing statistics built causally from the tape and\n",
+    "standardized with constants frozen on a burn-in window, a construction that\n",
+    "is out of scope here and easy to get subtly wrong. $B(h)$ is the first two sine/cosine harmonics of hour-of-day,\n",
+    "a low-order version of the Fourier encoding of intraday periodicity in\n",
+    "{cite:t}`andersen1997intraday`; heavy-tailed Student-t noise for returns goes\n",
+    "back at least to {cite:t}`bollerslev1987conditionally`. All symbol effects\n",
+    "$b_s \\sim \\mathcal{N}(0, \\tau)$ are partially pooled {cite:p}`gelman2006data`,\n",
+    "so a thin symbol is pulled toward the global intraday shape where its own data\n",
+    "run out. They are parameterized\n",
+    "*centered*, deliberately: the non-centered trick pays off when groups are\n",
+    "data-poor, and the generator below gives every symbol at least several hundred\n",
+    "rows.\n",
+    "The degrees of freedom are\n",
+    "shared across symbols, parameterized $\\nu = 1 + \\operatorname{softplus}(\\eta)$;\n",
+    "the floor of 1 rather than 2 leaves the support open to tails too heavy to\n",
+    "carry a finite variance. Under $\\eta \\sim \\mathcal{N}(5, 1)$ the prior puts\n",
+    "only about $4 \\times 10^{-6}$ of\n",
+    "its mass on $\\nu \\le 2$, so this is a statement about support, not a serious\n",
+    "prior belief in infinite variance. The estimand below is chosen so that the\n",
+    "question does not arise at all.\n",
+    "\n",
+    "The reported estimand is **event-return dispersion on the event clock**:\n",
+    "$\\pi_{s,h}$ together with the conditional-move 90% half-width\n",
+    "$\\sigma_{s,h} \\cdot t^{-1}_{0.95}(\\nu)$. A quantile is the safe choice here: it\n",
+    "stays finite for any $\\nu > 0$, whereas a moment-based dispersion requires\n",
+    "$\\nu > 2$, a constraint the model does not impose, even though the generator\n",
+    "below happens to use $\\nu = 3.5$."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c838d65",
+   "metadata": {},
+   "source": [
+    "## Synthetic data with known truth\n",
+    "\n",
+    "This notebook neither ships nor downloads an exchange archive; the executed\n",
+    "path uses a seeded synthetic generator: the schema an exchange\n",
+    "archive would have after feature construction, a hurdle at exactly zero,\n",
+    "heavy conditional tails, and twelve symbols whose row counts span two orders\n",
+    "of magnitude, so the hierarchy has both data-rich and data-poor groups to work\n",
+    "with. Because the truth is known, recovery is checkable. Every\n",
+    "statement below about what the fit recovers is conditional on this generator;\n",
+    "none of it is a measurement of any market."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "9e033f86",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:12:41.218724Z",
+     "iopub.status.busy": "2026-08-18T19:12:41.218500Z",
+     "iopub.status.idle": "2026-08-18T19:12:41.222167Z",
+     "shell.execute_reply": "2026-08-18T19:12:41.221590Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "n_symbols = 12\n",
+    "counts = np.array(\n",
+    "    [90_700, 55_000, 40_000, 30_000, 24_000, 19_000, 15_000, 11_000, 8_000, 4_600, 1_800, 900]\n",
+    ")\n",
+    "thin = [10, 11]  # the two symbols with the least data\n",
+    "\n",
+    "truth = {\n",
+    "    \"kappa0\": np.log(0.7 / 0.3),  # logit(0.7): see the annotation below\n",
+    "    \"c\": np.array([0.25, -0.15, 0.10, 0.05]),\n",
+    "    \"lambda_a\": 0.35,\n",
+    "    \"lambda_q\": 0.20,\n",
+    "    \"alpha0\": np.log(0.05),  # conditional-move scale, in basis points\n",
+    "    \"g\": np.array([0.20, -0.12, 0.08, 0.04]),\n",
+    "    \"beta_a\": 0.18,\n",
+    "    \"beta_q\": 0.12,\n",
+    "    \"theta_d\": 0.02,\n",
+    "    \"theta_r\": 0.25,\n",
+    "    \"nu\": 3.5,\n",
+    "}\n",
+    "\n",
+    "\n",
+    "def standardize(x, axis=0):\n",
+    "    return (x - x.mean(axis=axis, keepdims=True)) / x.std(axis=axis, keepdims=True)\n",
+    "\n",
+    "\n",
+    "z_truth = {\n",
+    "    \"z_k\": standardize(rng.standard_normal(n_symbols)),\n",
+    "    \"z_ph\": standardize(rng.standard_normal((n_symbols, 4))),\n",
+    "    \"z_al\": standardize(rng.standard_normal(n_symbols)),\n",
+    "    \"z_sh\": standardize(rng.standard_normal((n_symbols, 4))),\n",
+    "    \"z_ba\": standardize(rng.standard_normal(n_symbols)),\n",
+    "}"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "76034f5c",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:12:41.223540Z",
+     "iopub.status.busy": "2026-08-18T19:12:41.223446Z",
+     "iopub.status.idle": "2026-08-18T19:12:41.456631Z",
+     "shell.execute_reply": "2026-08-18T19:12:41.455855Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "300,000 rows, zero-move share 32.1%, median nonzero |y| 0.041 bp, corr(a, q) 0.30\n"
+     ]
+    }
+   ],
+   "source": [
+    "def hour_basis(hour):\n",
+    "    w = 2 * np.pi * np.asarray(hour, dtype=float) / 24.0\n",
+    "    return np.column_stack([np.sin(w), np.cos(w), np.sin(2 * w), np.cos(2 * w)])\n",
+    "\n",
+    "\n",
+    "sym = np.repeat(np.arange(n_symbols), counts)\n",
+    "n = len(sym)\n",
+    "hour = rng.integers(0, 24, size=n)\n",
+    "d = rng.choice([-1.0, 1.0], size=n)\n",
+    "a = standardize(rng.standard_normal(n))  # trailing activity (already standardized)\n",
+    "q = standardize(0.3 * a + np.sqrt(1 - 0.3**2) * rng.standard_normal(n))\n",
+    "\n",
+    "B = hour_basis(hour)\n",
+    "logit_pi = (\n",
+    "    truth[\"kappa0\"]\n",
+    "    + 0.30 * z_truth[\"z_k\"][sym]\n",
+    "    + B @ truth[\"c\"]\n",
+    "    + (B * (0.15 * z_truth[\"z_ph\"][sym])).sum(1)\n",
+    "    + truth[\"lambda_a\"] * a\n",
+    "    + truth[\"lambda_q\"] * q\n",
+    ")\n",
+    "log_sigma = (\n",
+    "    truth[\"alpha0\"]\n",
+    "    + 0.35 * z_truth[\"z_al\"][sym]\n",
+    "    + B @ truth[\"g\"]\n",
+    "    + (B * (0.12 * z_truth[\"z_sh\"][sym])).sum(1)\n",
+    "    + (truth[\"beta_a\"] + 0.10 * z_truth[\"z_ba\"][sym]) * a\n",
+    "    + truth[\"beta_q\"] * q\n",
+    ")\n",
+    "\n",
+    "m = (rng.random(n) < 1 / (1 + np.exp(-logit_pi))).astype(np.int8)\n",
+    "t_draw = rng.standard_t(truth[\"nu\"], size=n)\n",
+    "\n",
+    "# sequential generation per symbol so ylag feeds back causally\n",
+    "y = np.zeros(n)\n",
+    "ylag = np.zeros(n)\n",
+    "sigma = np.exp(log_sigma)\n",
+    "for s in range(n_symbols):\n",
+    "    idx = np.flatnonzero(sym == s)\n",
+    "    last = 0.0\n",
+    "    for i in idx:\n",
+    "        ylag[i] = last\n",
+    "        if m[i]:\n",
+    "            y[i] = truth[\"theta_d\"] * d[i] + truth[\"theta_r\"] * last + sigma[i] * t_draw[i]\n",
+    "            last = y[i]\n",
+    "\n",
+    "print(\n",
+    "    f\"{n:,} rows, zero-move share {(m == 0).mean():.1%}, \"\n",
+    "    f\"median nonzero |y| {np.median(np.abs(y[m == 1])):.3f} bp, \"\n",
+    "    f\"corr(a, q) {np.corrcoef(a, q)[0, 1]:.2f}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "eebd79a1",
+   "metadata": {},
+   "source": [
+    "The constants above are settings, not measurements. Read them as the\n",
+    "specification of the simulator:\n",
+    "\n",
+    "* `kappa0` = $\\operatorname{logit}(0.7)$. If every other term in the logit were\n",
+    "  zero, the move probability would be 0.7. The harmonics, the covariates and\n",
+    "  the symbol effects all shift it, so the *realized* zero share is the number\n",
+    "  printed above, not $30\\%$ by construction.\n",
+    "* `theta_r` $= 0.25$ imposes positive first-order dependence in the conditional\n",
+    "  location: the next move is generated partly from the previous nonzero one. It\n",
+    "  is a synthetic dependence parameter; note that the classical bid–ask bounce\n",
+    "  of {cite:t}`roll1984simple` runs the other way, inducing *negative* serial\n",
+    "  dependence, which this generator does not encode.\n",
+    "* `theta_d` $= 0.02$ is the coefficient on the simulated trade sign: holding\n",
+    "  everything else fixed, sign $+1$ versus $-1$ differs by 0.04 bp in\n",
+    "  conditional location.\n",
+    "* $a$ and $q$ are constructed with a target correlation of 0.3 rather than\n",
+    "  orthogonally, so the fit faces mildly collinear covariates instead of a\n",
+    "  textbook design; the realized sample correlation is in the printout above.\n",
+    "* `ylag` is generated per symbol in sequence, so row $i$ sees only the most\n",
+    "  recent previously generated nonzero move. That discipline applies to the lag\n",
+    "  construction; the standardization of $a$ and $q$ still uses full-sample\n",
+    "  constants, an offline simplification kept for readability.\n",
+    "\n",
+    "With that in mind, here is the feature that forces the hurdle:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "555034e3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:12:41.458672Z",
+     "iopub.status.busy": "2026-08-18T19:12:41.458538Z",
+     "iopub.status.idle": "2026-08-18T19:12:42.128111Z",
+     "shell.execute_reply": "2026-08-18T19:12:42.127491Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 361,
+       "width": 869
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(8, 3.5), layout=\"constrained\")\n",
+    "moves = y[m == 1]\n",
+    "ax.hist(moves, bins=201, range=(-1.5, 1.5), log=True, color=\"C0\", label=\"nonzero moves\")\n",
+    "ax.bar(\n",
+    "    [0.0],\n",
+    "    [(m == 0).sum()],\n",
+    "    width=0.02,\n",
+    "    color=\"C1\",\n",
+    "    label=f\"exactly zero ({(m == 0).mean():.0%} of rows)\",\n",
+    ")\n",
+    "ax.set_xlabel(\"next-event return (bp)\")\n",
+    "ax.set_ylabel(\"count (log scale)\")\n",
+    "ax.set_title(\"A continuous density puts zero mass on the most common outcome\")\n",
+    "ax.legend();"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "cc615068",
+   "metadata": {},
+   "source": [
+    "## The on-disk global shuffle\n",
+    "\n",
+    "Streaming ordered data has one trap that is easy to miss. A bounded runtime shuffle buffer only *block*-shuffles a strongly\n",
+    "ordered stream: with tick data sorted by symbol and time, early optimization\n",
+    "steps would only ever see early dates and the first symbols, and an early\n",
+    "stopping decision would be biased by construction. The fix is to shuffle\n",
+    "**once, globally, on disk when the archive is written**: every row gets a deterministic hash\n",
+    "key, rows are scattered across shards by that key, and each shard is sorted by\n",
+    "it. After that, sequential reads follow one fixed, data-independent\n",
+    "permutation. It is pseudo-random (a hash of the row index, nothing from the\n",
+    "row itself) and replayed identically each epoch, so this is single-shuffle stochastic gradient descent\n",
+    "rather than fresh per-step subsampling. The loader can then run with\n",
+    "`shuffle=False`, without a copy through the shuffle buffer."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "49e21042",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:12:42.129376Z",
+     "iopub.status.busy": "2026-08-18T19:12:42.129282Z",
+     "iopub.status.idle": "2026-08-18T19:12:42.412920Z",
+     "shell.execute_reply": "2026-08-18T19:12:42.412246Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "first 10k rows of every shard cover all 24 hours and all 12 symbols\n"
+     ]
+    }
+   ],
+   "source": [
+    "data_dir = tempfile.mkdtemp(prefix=\"ticks_\")\n",
+    "table = pa.table(\n",
+    "    {\n",
+    "        \"y_bp\": y.astype(np.float32),\n",
+    "        \"m\": m,\n",
+    "        \"d\": d.astype(np.int8),\n",
+    "        \"q_std\": q.astype(np.float32),\n",
+    "        \"a_std\": a.astype(np.float32),\n",
+    "        \"ylag_bp\": ylag.astype(np.float32),\n",
+    "        \"hour\": hour.astype(np.int8),\n",
+    "        \"sym\": sym.astype(np.int16),\n",
+    "    }\n",
+    ")\n",
+    "\n",
+    "\n",
+    "def splitmix64(x):\n",
+    "    x = (x + np.uint64(0x9E3779B97F4A7C15)) & np.uint64(0xFFFFFFFFFFFFFFFF)\n",
+    "    x = ((x ^ (x >> np.uint64(30))) * np.uint64(0xBF58476D1CE4E5B9)) & np.uint64(0xFFFFFFFFFFFFFFFF)\n",
+    "    x = ((x ^ (x >> np.uint64(27))) * np.uint64(0x94D049BB133111EB)) & np.uint64(0xFFFFFFFFFFFFFFFF)\n",
+    "    return x ^ (x >> np.uint64(31))\n",
+    "\n",
+    "\n",
+    "key = splitmix64(np.arange(n, dtype=np.uint64))\n",
+    "order = np.argsort(key, kind=\"stable\")\n",
+    "n_shards, BATCH = 10, 1_000\n",
+    "# Row groups are the loader's batches on the shuffle=False path, so the geometry is\n",
+    "# chosen to divide exactly: 10 shards x 30,000 rows, 30 groups of 1,000 each.\n",
+    "assert n % n_shards == 0 and (n // n_shards) % BATCH == 0\n",
+    "for i in range(n_shards):\n",
+    "    part = table.take(order[i::n_shards])\n",
+    "    pq.write_table(part, os.path.join(data_dir, f\"shard_{i:03d}.parquet\"), row_group_size=BATCH)\n",
+    "\n",
+    "# the head of every shard has to mix hours and symbols already; check all of them\n",
+    "for i in range(n_shards):\n",
+    "    head = pq.read_table(os.path.join(data_dir, f\"shard_{i:03d}.parquet\")).slice(0, 10_000)\n",
+    "    assert len(np.unique(head[\"hour\"])) == 24\n",
+    "    assert len(np.unique(head[\"sym\"])) == n_symbols\n",
+    "print(f\"first 10k rows of every shard cover all 24 hours and all {n_symbols} symbols\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a8999f22",
+   "metadata": {},
+   "source": [
+    "The assertion is the one to keep: the head of every shard must already mix\n",
+    "every hour and every symbol, checked rather than assumed.\n",
+    "\n",
+    "This cell is a small-scale stand-in for the real extract-transform-load step: it builds the whole\n",
+    "table, the whole key array and the whole permutation in memory, which is\n",
+    "exactly what one cannot do once the data stops fitting. The inference that\n",
+    "follows really does read from disk; the preprocessing above does not. An out-of-core design with the same properties is two passes:\n",
+    "assign each row to a shard from its hash key, appending row groups of bounded\n",
+    "size, then sort each shard by key independently, so no step holds more than\n",
+    "one shard. That yields a different permutation from the rank-based split used\n",
+    "here (shard sizes come out approximately rather than exactly equal), with the\n",
+    "same guarantee that no shard's order depends on anything in the rows.\n",
+    "\n",
+    "So that the fit really does run against disk rather than against arrays still\n",
+    "resident from the generator, the row-scale objects are released first:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "a4653e9e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:12:42.414406Z",
+     "iopub.status.busy": "2026-08-18T19:12:42.414289Z",
+     "iopub.status.idle": "2026-08-18T19:12:42.464157Z",
+     "shell.execute_reply": "2026-08-18T19:12:42.463290Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "row-scale generator arrays released; the fit reads from ticks_nihg_s5h\n"
+     ]
+    }
+   ],
+   "source": [
+    "del table, part, head, key, order, y, m, d, q, a, ylag, sym, hour, B, moves\n",
+    "del logit_pi, log_sigma, t_draw, sigma\n",
+    "gc.collect()\n",
+    "print(\"row-scale generator arrays released; the fit reads from\", os.path.basename(data_dir))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b6573ce7",
+   "metadata": {},
+   "source": [
+    "## Streaming the model\n",
+    "\n",
+    "With `shuffle=False` the `DataLoader` passes source blocks through verbatim,\n",
+    "one block per Parquet row group, in a frozen column order. That is why the\n",
+    "shards above were written with `row_group_size` equal to the batch size, and\n",
+    "why the row counts were chosen to divide exactly. On this path the loader\n",
+    "hands you the row groups it finds. A ragged geometry therefore gives ragged\n",
+    "batches; `len(loader)` (which is `total_size // batch_size`) stops matching the\n",
+    "number of blocks an epoch yields; and the recorded loss picks up a\n",
+    "deterministic sawtooth that has nothing to do with convergence (the fitting\n",
+    "section explains why the recorded value scales with the block size). The model reads\n",
+    "one `pm.Data` placeholder; everything derived (the Fourier basis, the\n",
+    "integer symbol index) is computed inside the graph, so advancing the stream\n",
+    "is a single `set_value` per step."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "b1c4c88d",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:12:42.465684Z",
+     "iopub.status.busy": "2026-08-18T19:12:42.465559Z",
+     "iopub.status.idle": "2026-08-18T19:12:42.557073Z",
+     "shell.execute_reply": "2026-08-18T19:12:42.556530Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "N = 300,000 rows -> 300 blocks of 1000 per epoch,\n",
+      "conserving 300,000 rows; len(loader) = 300\n"
+     ]
+    }
+   ],
+   "source": [
+    "columns = [\"y_bp\", \"m\", \"d\", \"q_std\", \"a_std\", \"ylag_bp\", \"hour\", \"sym\"]\n",
+    "loader = DataLoader(\n",
+    "    parquet_source(data_dir, columns=columns),\n",
+    "    batch_size=BATCH,\n",
+    "    shuffle=False,  # the shards are already globally shuffled on disk\n",
+    "    total_size=\"auto\",\n",
+    ")\n",
+    "\n",
+    "# Count one epoch rather than inferring it. With a divisible geometry every block\n",
+    "# is the same size, so the count and len(loader) agree — which is what the rest of\n",
+    "# the notebook relies on.\n",
+    "block_rows = [b.shape[0] for b in loader]\n",
+    "steps_per_epoch = len(block_rows)\n",
+    "assert set(block_rows) == {BATCH}\n",
+    "assert sum(block_rows) == loader.total_size == n == steps_per_epoch * BATCH\n",
+    "assert len(loader) == steps_per_epoch\n",
+    "print(\n",
+    "    f\"N = {loader.total_size:,} rows -> {steps_per_epoch} blocks of {BATCH} per epoch,\\n\"\n",
+    "    f\"conserving {sum(block_rows):,} rows; len(loader) = {len(loader)}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "229033e5",
+   "metadata": {},
+   "source": [
+    "That assertion pins down two things. First, **nothing is dropped**: verbatim\n",
+    "pass-through streams every row exactly once per epoch. (Ragged geometry would\n",
+    "still lose nothing, since PyMC reads `b` from the batch actually installed and\n",
+    "a short block is weighted up by its own size, but it would cost the clean\n",
+    "diagnostics below, which is why the shards divide.) Only the shuffle-buffer\n",
+    "path drops a trailing partial batch, and this notebook never uses it.\n",
+    "\n",
+    "Second, this is where the Gate 1 distinction bites: the batch at step $t$ is a\n",
+    "deterministic function of $t$, so what follows is single-shuffle, cyclic\n",
+    "finite-sum optimization, the arrangement the epoch-scale diagnostics later in\n",
+    "the notebook exploit and the reason the fit has to be summarized with some\n",
+    "care before anything is read off it."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "c959355b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:12:42.558484Z",
+     "iopub.status.busy": "2026-08-18T19:12:42.558367Z",
+     "iopub.status.idle": "2026-08-18T19:12:42.565543Z",
+     "shell.execute_reply": "2026-08-18T19:12:42.564821Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def build_model(symbols, batch_init, total_size):\n",
+    "    coords = {\"symbol\": list(symbols), \"harmonic\": [\"sin1\", \"cos1\", \"sin2\", \"cos2\"]}\n",
+    "    with pm.Model(coords=coords) as model:\n",
+    "        batch = pm.Data(\"batch\", batch_init)\n",
+    "        y_ = batch[:, 0]\n",
+    "        d_ = batch[:, 2]\n",
+    "        q_ = batch[:, 3]\n",
+    "        a_ = batch[:, 4]\n",
+    "        ylag_ = batch[:, 5]\n",
+    "        w = 2.0 * np.pi * batch[:, 6] / 24.0\n",
+    "        B_ = pt.stack([pt.sin(w), pt.cos(w), pt.sin(2 * w), pt.cos(2 * w)], axis=1)\n",
+    "        sym_ = pt.cast(batch[:, 7], \"int32\")\n",
+    "\n",
+    "        kappa0 = pm.Normal(\"kappa0\", 0.0, 5.0)\n",
+    "        c = pm.Normal(\"c\", 0.0, 0.5, dims=\"harmonic\")\n",
+    "        lambda_a = pm.Normal(\"lambda_a\", 0.0, 0.5)\n",
+    "        lambda_q = pm.Normal(\"lambda_q\", 0.0, 0.5)\n",
+    "\n",
+    "        alpha0 = pm.Normal(\"alpha0\", 0.0, 5.0)\n",
+    "        g = pm.Normal(\"g\", 0.0, 0.3, dims=\"harmonic\")\n",
+    "        beta_a = pm.Normal(\"beta_a\", 0.0, 0.3)\n",
+    "        beta_q = pm.Normal(\"beta_q\", 0.0, 0.3)\n",
+    "\n",
+    "        theta_d = pm.Normal(\"theta_d\", 0.0, 0.25)\n",
+    "        theta_r = pm.Normal(\"theta_r\", 0.0, 0.25)\n",
+    "\n",
+    "        tau_k, tau_ph, tau_al, tau_sh, tau_ba = (\n",
+    "            pm.LogNormal(name, 0.0, 1.5)\n",
+    "            for name in [\"tau_k\", \"tau_ph\", \"tau_al\", \"tau_sh\", \"tau_ba\"]\n",
+    "        )\n",
+    "        b_k = pm.Normal(\"b_k\", 0.0, tau_k, dims=\"symbol\")\n",
+    "        b_ph = pm.Normal(\"b_ph\", 0.0, tau_ph, dims=(\"symbol\", \"harmonic\"))\n",
+    "        b_al = pm.Normal(\"b_al\", 0.0, tau_al, dims=\"symbol\")\n",
+    "        b_sh = pm.Normal(\"b_sh\", 0.0, tau_sh, dims=(\"symbol\", \"harmonic\"))\n",
+    "        b_ba = pm.Normal(\"b_ba\", 0.0, tau_ba, dims=\"symbol\")\n",
+    "\n",
+    "        eta = pm.Normal(\"eta\", 5.0, 1.0)\n",
+    "        nu = pm.Deterministic(\"nu\", 1.0 + pt.softplus(eta))\n",
+    "\n",
+    "        logit_pi = (\n",
+    "            kappa0\n",
+    "            + b_k[sym_]\n",
+    "            + (B_ * (c + b_ph[sym_])).sum(axis=-1)\n",
+    "            + lambda_a * a_\n",
+    "            + lambda_q * q_\n",
+    "        )\n",
+    "        log_sigma = (\n",
+    "            alpha0\n",
+    "            + b_al[sym_]\n",
+    "            + (B_ * (g + b_sh[sym_])).sum(axis=-1)\n",
+    "            + (beta_a + b_ba[sym_]) * a_\n",
+    "            + beta_q * q_\n",
+    "        )\n",
+    "        mu = theta_d * d_ + theta_r * ylag_\n",
+    "\n",
+    "        def hurdle_logp(value, logit_pi, mu, log_sigma, nu):\n",
+    "            # one coherent mixed distribution: an atom at exactly zero plus a\n",
+    "            # Student-t density off zero — the move indicator is value != 0,\n",
+    "            # never a separate parameter, so logp and random describe the SAME law\n",
+    "            sigma = pt.exp(log_sigma)\n",
+    "            t_ll = (\n",
+    "                pt.gammaln((nu + 1.0) / 2.0)\n",
+    "                - pt.gammaln(nu / 2.0)\n",
+    "                - 0.5 * pt.log(nu * np.pi)\n",
+    "                - log_sigma\n",
+    "                - (nu + 1.0) / 2.0 * pt.log1p(((value - mu) / sigma) ** 2 / nu)\n",
+    "            )\n",
+    "            moved = pt.neq(value, 0.0)\n",
+    "            return pt.where(moved, -pt.softplus(-logit_pi) + t_ll, -pt.softplus(logit_pi))\n",
+    "\n",
+    "        def hurdle_random(logit_pi, mu, log_sigma, nu, rng=None, size=None):\n",
+    "            # simulate the same law: first whether the price moves, then how far\n",
+    "            pi = 1.0 / (1.0 + np.exp(-logit_pi))\n",
+    "            move = rng.random(size=size) < pi\n",
+    "            draw = mu + np.exp(log_sigma) * rng.standard_t(nu, size=size)\n",
+    "            return np.where(move, draw, 0.0)\n",
+    "\n",
+    "        pm.CustomDist(\n",
+    "            \"y_obs\",\n",
+    "            logit_pi,\n",
+    "            mu,\n",
+    "            log_sigma,\n",
+    "            nu,\n",
+    "            logp=hurdle_logp,\n",
+    "            random=hurdle_random,\n",
+    "            observed=y_,\n",
+    "            total_size=total_size,\n",
+    "        )\n",
+    "    return model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "03837162",
+   "metadata": {},
+   "source": [
+    "Two implementation notes. The likelihood is a\n",
+    "{class}`~pymc.CustomDist` with a `logp`, not a `pm.Potential`: `CustomDist`\n",
+    "gives the term *observed random-variable semantics*, which is what makes PyMC's own\n",
+    "`total_size` minibatch rescaling apply. And\n",
+    "the hurdle's two logs are written with `softplus`,\n",
+    "$\\log \\pi = -\\operatorname{softplus}(-x)$ and\n",
+    "$\\log(1-\\pi) = -\\operatorname{softplus}(x)$, which is exact and stable at both\n",
+    "tails. The exact float comparison `value != 0` is safe here because the zeros are\n",
+    "*structural*: the generator writes an exact 0.0 when the hurdle says the price\n",
+    "did not move, and every nonzero draw is many orders of magnitude above the\n",
+    "float32 subnormal range. On real data the same comparison is safe as long as\n",
+    "returns are computed so that \"no move\" produces an exact zero rather than a\n",
+    "rounding artifact; check that once, in the feature code."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "3350f3f4",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:12:42.567066Z",
+     "iopub.status.busy": "2026-08-18T19:12:42.566950Z",
+     "iopub.status.idle": "2026-08-18T19:12:42.773988Z",
+     "shell.execute_reply": "2026-08-18T19:12:42.773477Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/svg+xml": [
+       "<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\"?>\n",
+       "<!DOCTYPE svg PUBLIC \"-//W3C//DTD SVG 1.1//EN\"\n",
+       " \"http://www.w3.org/Graphics/SVG/1.1/DTD/svg11.dtd\">\n",
+       "<!-- Generated by graphviz version 15.1.0 (20260618.0150)\n",
+       " -->\n",
+       "<!-- Pages: 1 -->\n",
+       "<svg width=\"1705pt\" height=\"483pt\"\n",
+       " viewBox=\"0.00 0.00 1705.00 483.00\" xmlns=\"http://www.w3.org/2000/svg\" xmlns:xlink=\"http://www.w3.org/1999/xlink\">\n",
+       "<g id=\"graph0\" class=\"graph\" transform=\"scale(1 1) rotate(0) translate(4 478.95)\">\n",
+       "<polygon fill=\"white\" stroke=\"none\" points=\"-4,4 -4,-478.95 1701.28,-478.95 1701.28,4 -4,4\"/>\n",
+       "<g id=\"clust1\" class=\"cluster\">\n",
+       "<title>cluster1000 x 8</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M810.01,-8C810.01,-8 856.01,-8 856.01,-8 862.01,-8 868.01,-14 868.01,-20 868.01,-20 868.01,-94 868.01,-94 868.01,-100 862.01,-106 856.01,-106 856.01,-106 810.01,-106 810.01,-106 804.01,-106 798.01,-100 798.01,-94 798.01,-94 798.01,-20 798.01,-20 798.01,-14 804.01,-8 810.01,-8\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"836.01\" y=\"-15.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000 x 8</text>\n",
+       "</g>\n",
+       "<g id=\"clust2\" class=\"cluster\">\n",
+       "<title>clusterharmonic (4)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M946.01,-243.82C946.01,-243.82 1120.01,-243.82 1120.01,-243.82 1126.01,-243.82 1132.01,-249.82 1132.01,-255.82 1132.01,-255.82 1132.01,-353.63 1132.01,-353.63 1132.01,-359.63 1126.01,-365.63 1120.01,-365.63 1120.01,-365.63 946.01,-365.63 946.01,-365.63 940.01,-365.63 934.01,-359.63 934.01,-353.63 934.01,-353.63 934.01,-255.82 934.01,-255.82 934.01,-249.82 940.01,-243.82 946.01,-243.82\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1088.39\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">harmonic (4)</text>\n",
+       "</g>\n",
+       "<g id=\"clust3\" class=\"cluster\">\n",
+       "<title>clustersymbol (12)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1152.01,-243.82C1152.01,-243.82 1426.01,-243.82 1426.01,-243.82 1432.01,-243.82 1438.01,-249.82 1438.01,-255.82 1438.01,-255.82 1438.01,-353.63 1438.01,-353.63 1438.01,-359.63 1432.01,-365.63 1426.01,-365.63 1426.01,-365.63 1152.01,-365.63 1152.01,-365.63 1146.01,-365.63 1140.01,-359.63 1140.01,-353.63 1140.01,-353.63 1140.01,-255.82 1140.01,-255.82 1140.01,-249.82 1146.01,-243.82 1152.01,-243.82\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1396.64\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12)</text>\n",
+       "</g>\n",
+       "<g id=\"clust4\" class=\"cluster\">\n",
+       "<title>clustersymbol (12) x harmonic (4)</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1469.01,-243.82C1469.01,-243.82 1643.01,-243.82 1643.01,-243.82 1649.01,-243.82 1655.01,-249.82 1655.01,-255.82 1655.01,-255.82 1655.01,-353.63 1655.01,-353.63 1655.01,-359.63 1649.01,-365.63 1643.01,-365.63 1643.01,-365.63 1469.01,-365.63 1469.01,-365.63 1463.01,-365.63 1457.01,-359.63 1457.01,-353.63 1457.01,-353.63 1457.01,-255.82 1457.01,-255.82 1457.01,-249.82 1463.01,-243.82 1469.01,-243.82\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1570.89\" y=\"-251.02\" font-family=\"Times,serif\" font-size=\"14.00\">symbol (12) x harmonic (4)</text>\n",
+       "</g>\n",
+       "<g id=\"clust5\" class=\"cluster\">\n",
+       "<title>cluster1000</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M721.01,-114C721.01,-114 945.01,-114 945.01,-114 951.01,-114 957.01,-120 957.01,-126 957.01,-126 957.01,-223.82 957.01,-223.82 957.01,-229.82 951.01,-235.82 945.01,-235.82 945.01,-235.82 721.01,-235.82 721.01,-235.82 715.01,-235.82 709.01,-229.82 709.01,-223.82 709.01,-223.82 709.01,-126 709.01,-126 709.01,-120 715.01,-114 721.01,-114\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"935.51\" y=\"-121.2\" font-family=\"Times,serif\" font-size=\"14.00\">1000</text>\n",
+       "</g>\n",
+       "<!-- batch -->\n",
+       "<g id=\"node1\" class=\"node\">\n",
+       "<title>batch</title>\n",
+       "<path fill=\"lightgrey\" stroke=\"black\" d=\"M848.01,-98C848.01,-98 818.01,-98 818.01,-98 812.01,-98 806.01,-92 806.01,-86 806.01,-86 806.01,-52.5 806.01,-52.5 806.01,-46.5 812.01,-40.5 818.01,-40.5 818.01,-40.5 848.01,-40.5 848.01,-40.5 854.01,-40.5 860.01,-46.5 860.01,-52.5 860.01,-52.5 860.01,-86 860.01,-86 860.01,-92 854.01,-98 848.01,-98\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-80.7\" font-family=\"Times,serif\" font-size=\"14.00\">batch</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-64.2\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-47.7\" font-family=\"Times,serif\" font-size=\"14.00\">Data</text>\n",
+       "</g>\n",
+       "<!-- alpha0 -->\n",
+       "<g id=\"node2\" class=\"node\">\n",
+       "<title>alpha0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"41.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">alpha0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"41.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- y_obs -->\n",
+       "<g id=\"node24\" class=\"node\">\n",
+       "<title>y_obs</title>\n",
+       "<ellipse fill=\"lightgrey\" stroke=\"black\" cx=\"833.01\" cy=\"-187.16\" rx=\"115.79\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-198.61\" font-family=\"Times,serif\" font-size=\"14.00\">y_obs</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-182.11\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"833.01\" y=\"-165.61\" font-family=\"Times,serif\" font-size=\"14.00\">MinibatchRandomVariable</text>\n",
+       "</g>\n",
+       "<!-- alpha0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge14\" class=\"edge\">\n",
+       "<title>alpha0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M57.73,-279.33C65.8,-265.85 76.86,-251.92 91.01,-243.82 142.94,-214.09 508.37,-198.24 706.31,-191.75\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"706.25,-195.25 716.13,-191.43 706.02,-188.26 706.25,-195.25\"/>\n",
+       "</g>\n",
+       "<!-- theta_d -->\n",
+       "<g id=\"node3\" class=\"node\">\n",
+       "<title>theta_d</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"141.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_d</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"141.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- theta_d&#45;&gt;y_obs -->\n",
+       "<g id=\"edge13\" class=\"edge\">\n",
+       "<title>theta_d&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M157.77,-279.41C165.86,-265.95 176.91,-252 191.01,-243.82 234.42,-218.63 532.39,-201.41 706.73,-193.36\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"706.69,-196.86 716.52,-192.91 706.37,-189.87 706.69,-196.86\"/>\n",
+       "</g>\n",
+       "<!-- nu -->\n",
+       "<g id=\"node4\" class=\"node\">\n",
+       "<title>nu</title>\n",
+       "<polygon fill=\"none\" stroke=\"black\" points=\"290.14,-345.73 199.89,-345.73 199.89,-288.23 290.14,-288.23 290.14,-345.73\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"245.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">nu</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"245.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"245.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Deterministic</text>\n",
+       "</g>\n",
+       "<!-- nu&#45;&gt;y_obs -->\n",
+       "<g id=\"edge8\" class=\"edge\">\n",
+       "<title>nu&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M258.52,-288.04C267.67,-272.31 281.32,-253.86 299.01,-243.82 366.19,-205.7 570.16,-193.61 705.67,-189.82\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"705.52,-193.33 715.42,-189.56 705.33,-186.33 705.52,-193.33\"/>\n",
+       "</g>\n",
+       "<!-- eta -->\n",
+       "<g id=\"node5\" class=\"node\">\n",
+       "<title>eta</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"245.01\" cy=\"-434.29\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"245.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">eta</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"245.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"245.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- eta&#45;&gt;nu -->\n",
+       "<g id=\"edge7\" class=\"edge\">\n",
+       "<title>eta&#45;&gt;nu</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M245.01,-393.28C245.01,-381.58 245.01,-368.79 245.01,-357.14\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"248.51,-357.45 245.01,-347.45 241.51,-357.45 248.51,-357.45\"/>\n",
+       "</g>\n",
+       "<!-- beta_q -->\n",
+       "<g id=\"node6\" class=\"node\">\n",
+       "<title>beta_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"349.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"349.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"349.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"349.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge10\" class=\"edge\">\n",
+       "<title>beta_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M366.44,-279.7C374.73,-266.27 385.95,-252.28 400.01,-243.82 450.19,-213.62 597.57,-199.6 706.51,-193.23\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"706.63,-196.73 716.41,-192.67 706.23,-189.74 706.63,-196.73\"/>\n",
+       "</g>\n",
+       "<!-- lambda_q -->\n",
+       "<g id=\"node7\" class=\"node\">\n",
+       "<title>lambda_q</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"458.01\" cy=\"-316.98\" rx=\"49.5\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"458.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_q</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"458.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"458.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- lambda_q&#45;&gt;y_obs -->\n",
+       "<g id=\"edge18\" class=\"edge\">\n",
+       "<title>lambda_q&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M479.52,-279.99C489.31,-266.61 502.07,-252.57 517.01,-243.82 549.64,-224.7 636.56,-210.21 710.99,-200.81\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"711.35,-204.29 720.84,-199.59 710.49,-197.34 711.35,-204.29\"/>\n",
+       "</g>\n",
+       "<!-- beta_a -->\n",
+       "<g id=\"node8\" class=\"node\">\n",
+       "<title>beta_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"567.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"567.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">beta_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"567.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"567.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- beta_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge19\" class=\"edge\">\n",
+       "<title>beta_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M584.74,-280.2C592.91,-267.01 603.76,-253.05 617.01,-243.82 644.99,-224.33 679.27,-211.66 712.21,-203.43\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"712.92,-206.86 721.85,-201.15 711.31,-200.04 712.92,-206.86\"/>\n",
+       "</g>\n",
+       "<!-- tau_sh -->\n",
+       "<g id=\"node9\" class=\"node\">\n",
+       "<title>tau_sh</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1516.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1516.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_sh -->\n",
+       "<g id=\"node22\" class=\"node\">\n",
+       "<title>b_sh</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1506.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_sh</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1506.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_sh&#45;&gt;b_sh -->\n",
+       "<g id=\"edge5\" class=\"edge\">\n",
+       "<title>tau_sh&#45;&gt;b_sh</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1512.54,-393.28C1511.87,-385.5 1511.15,-377.25 1510.45,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1513.96,-369.08 1509.61,-359.42 1506.98,-369.69 1513.96,-369.08\"/>\n",
+       "</g>\n",
+       "<!-- lambda_a -->\n",
+       "<g id=\"node10\" class=\"node\">\n",
+       "<title>lambda_a</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"675.01\" cy=\"-316.98\" rx=\"48.97\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"675.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">lambda_a</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"675.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"675.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- lambda_a&#45;&gt;y_obs -->\n",
+       "<g id=\"edge21\" class=\"edge\">\n",
+       "<title>lambda_a&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M698.56,-281.27C708.27,-268.53 720.24,-254.6 733.01,-243.82 740.22,-237.74 748.18,-232 756.39,-226.69\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"758.16,-229.71 764.8,-221.45 754.46,-223.76 758.16,-229.71\"/>\n",
+       "</g>\n",
+       "<!-- kappa0 -->\n",
+       "<g id=\"node11\" class=\"node\">\n",
+       "<title>kappa0</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"783.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"783.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">kappa0</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"783.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"783.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- kappa0&#45;&gt;y_obs -->\n",
+       "<g id=\"edge22\" class=\"edge\">\n",
+       "<title>kappa0&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M797.61,-278.66C802.57,-265.99 808.19,-251.61 813.47,-238.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"816.68,-239.52 817.06,-228.93 810.16,-236.97 816.68,-239.52\"/>\n",
+       "</g>\n",
+       "<!-- tau_k -->\n",
+       "<g id=\"node12\" class=\"node\">\n",
+       "<title>tau_k</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1135.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1135.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_k -->\n",
+       "<g id=\"node21\" class=\"node\">\n",
+       "<title>b_k</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1189.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_k</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1189.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_k&#45;&gt;b_k -->\n",
+       "<g id=\"edge2\" class=\"edge\">\n",
+       "<title>tau_k&#45;&gt;b_k</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1152.69,-395.53C1157.35,-385.58 1162.44,-374.73 1167.28,-364.38\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1170.31,-366.16 1171.38,-355.62 1163.97,-363.19 1170.31,-366.16\"/>\n",
+       "</g>\n",
+       "<!-- tau_al -->\n",
+       "<g id=\"node13\" class=\"node\">\n",
+       "<title>tau_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1262.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1262.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_al -->\n",
+       "<g id=\"node19\" class=\"node\">\n",
+       "<title>b_al</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1289.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_al</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1289.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_al&#45;&gt;b_al -->\n",
+       "<g id=\"edge4\" class=\"edge\">\n",
+       "<title>tau_al&#45;&gt;b_al</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1271.23,-393.93C1273.2,-385.49 1275.32,-376.46 1277.37,-367.69\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1280.71,-368.78 1279.58,-358.24 1273.9,-367.18 1280.71,-368.78\"/>\n",
+       "</g>\n",
+       "<!-- tau_ba -->\n",
+       "<g id=\"node14\" class=\"node\">\n",
+       "<title>tau_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_ba -->\n",
+       "<g id=\"node20\" class=\"node\">\n",
+       "<title>b_ba</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1389.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ba</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1389.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_ba&#45;&gt;b_ba -->\n",
+       "<g id=\"edge6\" class=\"edge\">\n",
+       "<title>tau_ba&#45;&gt;b_ba</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1389.01,-393.28C1389.01,-385.5 1389.01,-377.25 1389.01,-369.16\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1392.51,-369.43 1389.01,-359.43 1385.51,-369.43 1392.51,-369.43\"/>\n",
+       "</g>\n",
+       "<!-- theta_r -->\n",
+       "<g id=\"node15\" class=\"node\">\n",
+       "<title>theta_r</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"883.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">theta_r</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"883.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- theta_r&#45;&gt;y_obs -->\n",
+       "<g id=\"edge12\" class=\"edge\">\n",
+       "<title>theta_r&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M868.42,-278.66C863.46,-265.99 857.83,-251.61 852.55,-238.11\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"855.86,-236.97 848.96,-228.93 849.35,-239.52 855.86,-236.97\"/>\n",
+       "</g>\n",
+       "<!-- tau_ph -->\n",
+       "<g id=\"node16\" class=\"node\">\n",
+       "<title>tau_ph</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1643.01\" cy=\"-434.29\" rx=\"54.27\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-445.74\" font-family=\"Times,serif\" font-size=\"14.00\">tau_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-429.24\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1643.01\" y=\"-412.74\" font-family=\"Times,serif\" font-size=\"14.00\">Lognormal</text>\n",
+       "</g>\n",
+       "<!-- b_ph -->\n",
+       "<g id=\"node23\" class=\"node\">\n",
+       "<title>b_ph</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1606.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">b_ph</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1606.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- tau_ph&#45;&gt;b_ph -->\n",
+       "<g id=\"edge3\" class=\"edge\">\n",
+       "<title>tau_ph&#45;&gt;b_ph</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1630.49,-394.25C1627.68,-385.52 1624.68,-376.15 1621.76,-367.07\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"1625.13,-366.09 1618.74,-357.64 1618.46,-368.23 1625.13,-366.09\"/>\n",
+       "</g>\n",
+       "<!-- g -->\n",
+       "<g id=\"node17\" class=\"node\">\n",
+       "<title>g</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"983.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">g</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"983.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- g&#45;&gt;y_obs -->\n",
+       "<g id=\"edge11\" class=\"edge\">\n",
+       "<title>g&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M963.48,-281.13C955.21,-268.37 944.76,-254.46 933.01,-243.82 926.13,-237.58 918.44,-231.77 910.45,-226.43\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"912.56,-223.63 902.24,-221.2 908.79,-229.53 912.56,-223.63\"/>\n",
+       "</g>\n",
+       "<!-- c -->\n",
+       "<g id=\"node18\" class=\"node\">\n",
+       "<title>c</title>\n",
+       "<ellipse fill=\"none\" stroke=\"black\" cx=\"1083.01\" cy=\"-316.98\" rx=\"41.01\" ry=\"40.66\"/>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.01\" y=\"-328.43\" font-family=\"Times,serif\" font-size=\"14.00\">c</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.01\" y=\"-311.93\" font-family=\"Times,serif\" font-size=\"14.00\">~</text>\n",
+       "<text xml:space=\"preserve\" text-anchor=\"middle\" x=\"1083.01\" y=\"-295.43\" font-family=\"Times,serif\" font-size=\"14.00\">Normal</text>\n",
+       "</g>\n",
+       "<!-- c&#45;&gt;y_obs -->\n",
+       "<g id=\"edge23\" class=\"edge\">\n",
+       "<title>c&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1064.96,-279.96C1056.81,-266.9 1046.05,-253.09 1033.01,-243.82 1008.72,-226.55 979.26,-214.63 950.5,-206.4\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"951.55,-203.06 940.98,-203.82 949.72,-209.82 951.55,-203.06\"/>\n",
+       "</g>\n",
+       "<!-- b_al&#45;&gt;y_obs -->\n",
+       "<g id=\"edge9\" class=\"edge\">\n",
+       "<title>b_al&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1272.04,-279.77C1263.93,-266.35 1252.91,-252.35 1239.01,-243.82 1193.28,-215.75 1060.48,-201.43 959.09,-194.41\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"959.55,-190.93 949.34,-193.75 959.08,-197.91 959.55,-190.93\"/>\n",
+       "</g>\n",
+       "<!-- b_ba&#45;&gt;y_obs -->\n",
+       "<g id=\"edge20\" class=\"edge\">\n",
+       "<title>b_ba&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1372.15,-279.58C1364.06,-266.14 1353.02,-252.16 1339.01,-243.82 1277.15,-206.97 1089.01,-194.48 960.43,-190.27\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"960.57,-186.77 950.47,-189.96 960.35,-193.77 960.57,-186.77\"/>\n",
+       "</g>\n",
+       "<!-- b_k&#45;&gt;y_obs -->\n",
+       "<g id=\"edge16\" class=\"edge\">\n",
+       "<title>b_k&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1170.62,-280.44C1161.9,-266.96 1150.24,-252.73 1136.01,-243.82 1105.84,-224.92 1025.11,-210.63 954.56,-201.26\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"955.2,-197.81 944.84,-199.99 954.3,-204.75 955.2,-197.81\"/>\n",
+       "</g>\n",
+       "<!-- b_sh&#45;&gt;y_obs -->\n",
+       "<g id=\"edge15\" class=\"edge\">\n",
+       "<title>b_sh&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1484.51,-281.95C1473.6,-267.68 1459.05,-252.43 1442.01,-243.82 1360.56,-202.63 1113.78,-191.66 960.51,-188.9\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"960.76,-185.4 950.7,-188.73 960.64,-192.4 960.76,-185.4\"/>\n",
+       "</g>\n",
+       "<!-- b_ph&#45;&gt;y_obs -->\n",
+       "<g id=\"edge17\" class=\"edge\">\n",
+       "<title>b_ph&#45;&gt;y_obs</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M1589.29,-279.34C1581.21,-265.87 1570.16,-251.93 1556.01,-243.82 1505.72,-214.97 1153.47,-198.82 959.84,-192.03\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"960.09,-188.54 949.98,-191.69 959.85,-195.53 960.09,-188.54\"/>\n",
+       "</g>\n",
+       "<!-- y_obs&#45;&gt;batch -->\n",
+       "<g id=\"edge1\" class=\"edge\">\n",
+       "<title>y_obs&#45;&gt;batch</title>\n",
+       "<path fill=\"none\" stroke=\"black\" d=\"M833.01,-146.27C833.01,-134.38 833.01,-121.37 833.01,-109.53\"/>\n",
+       "<polygon fill=\"black\" stroke=\"black\" points=\"836.51,-109.67 833.01,-99.67 829.51,-109.67 836.51,-109.67\"/>\n",
+       "</g>\n",
+       "</g>\n",
+       "</svg>\n"
+      ],
+      "text/plain": [
+       "<graphviz.graphs.Digraph at 0x117bf5460>"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model = build_model(\n",
+    "    [f\"SYM{i:02d}\" for i in range(n_symbols)], next(iter(loader)), loader.total_size\n",
+    ")\n",
+    "pm.model_to_graphviz(model)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "6dd8e399",
+   "metadata": {},
+   "source": [
+    "The plate diagram is an inventory rather than a picture of the two links: five\n",
+    "partially pooled effect families on the symbol plate, one shared tail\n",
+    "parameter, and a single observed node whose batch dimension is whatever the\n",
+    "placeholder currently holds. The two linear predictors live inside the\n",
+    "likelihood's `logp` and are not separate nodes; the equations above are where\n",
+    "that structure is visible.\n",
+    "\n",
+    "The loop below is the whole streaming adapter: a `pm.fit` callback that pushes\n",
+    "the next block into the placeholder after each step; the twenty lines here are\n",
+    "the minimal version a notebook can own (a library wrapper for the same\n",
+    "lifecycle is listed at the end)."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "ce0fe1b9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:12:42.775471Z",
+     "iopub.status.busy": "2026-08-18T19:12:42.775327Z",
+     "iopub.status.idle": "2026-08-18T19:12:51.294991Z",
+     "shell.execute_reply": "2026-08-18T19:12:51.294432Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "class StreamAdvance:\n",
+    "    \"\"\"pm.fit callback: put the next minibatch into the placeholder after each step.\"\"\"\n",
+    "\n",
+    "    def __init__(self, model, loader):\n",
+    "        self._shared = model[\"batch\"]\n",
+    "        self._stream = self._endless(loader)\n",
+    "\n",
+    "    @staticmethod\n",
+    "    def _endless(loader):\n",
+    "        while True:\n",
+    "            yield from loader\n",
+    "\n",
+    "    def prime(self):\n",
+    "        self._shared.set_value(next(self._stream), borrow=True)\n",
+    "\n",
+    "    def __call__(self, approx, losses, i):\n",
+    "        self._shared.set_value(next(self._stream), borrow=True)\n",
+    "\n",
+    "\n",
+    "class ParamTrace:\n",
+    "    \"\"\"pm.fit callback: record the variational parameters after every step.\"\"\"\n",
+    "\n",
+    "    def __init__(self):\n",
+    "        self.mu, self.rho = [], []\n",
+    "\n",
+    "    def __call__(self, approx, losses, i):\n",
+    "        mu, rho = approx.params\n",
+    "        self.mu.append(mu.get_value())\n",
+    "        self.rho.append(rho.get_value())\n",
+    "\n",
+    "\n",
+    "stream = StreamAdvance(model, loader)\n",
+    "stream.prime()\n",
+    "tail = ParamTrace()\n",
+    "\n",
+    "with model:\n",
+    "    advi = pm.ADVI(random_seed=RANDOM_SEED)\n",
+    "advi.fit(6_000, obj_optimizer=pm.adam(learning_rate=0.02), callbacks=[stream], progressbar=False)\n",
+    "approx = advi.fit(\n",
+    "    2_400,\n",
+    "    obj_optimizer=pm.adam(learning_rate=0.005),\n",
+    "    callbacks=[stream, tail],\n",
+    "    progressbar=False,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "5b9f2ddc",
+   "metadata": {},
+   "source": [
+    "The fit is mean-field ADVI {cite:p}`kucukelbir2015automatic` driven by Adam.\n",
+    "The learning rate is cut once, at step 6,000. A constant step size that is\n",
+    "comfortable early is too large near the optimum, where it keeps the\n",
+    "variational mean bouncing instead of settling; dropping it once the descent\n",
+    "has flattened is the cheapest fix. The effect does not show up in the width of\n",
+    "the loss band below: the band is dominated by batch-composition noise (the\n",
+    "stopping section shows it all but vanishing at epoch-aligned horizons), and\n",
+    "the printed spread is the same on both sides of the change. It shows up in the\n",
+    "level, as the printed one-epoch means either side of the cut, and in the\n",
+    "parameter trace examined after the fit.\n",
+    "\n",
+    "Before plotting it, one property of `approx.hist` that is easy to get wrong and\n",
+    "that changes what the numbers mean. PyMC normalizes the minibatch objective\n",
+    "**twice**: the observed log-probability is scaled up by $N/b$ so the gradient\n",
+    "targets the full-data model, and then the variational objective is divided by\n",
+    "that same constant, because `scale_cost_to_minibatch` is on by default. What\n",
+    "gets recorded per step is therefore\n",
+    "\n",
+    "$$\n",
+    "F_t = -\\sum_{i \\in \\mathcal{B}_t} \\mathbb{E}_q\\left[\\log p(y_i \\mid \\theta)\\right]\n",
+    "      + \\frac{b}{N}\\,\\mathrm{KL}(q \\Vert p),\n",
+    "$$\n",
+    "\n",
+    "where $\\mathcal{B}_t$ is the set of rows in the batch at step $t$, $b$ its\n",
+    "size, $N$ the total row count (`loader.total_size`), $\\theta$ the parameters,\n",
+    "$q$ the variational approximation, $p$ the prior, and $\\mathrm{KL}$ the\n",
+    "Kullback–Leibler divergence. This lives on the scale of *one batch*, not of\n",
+    "the full dataset, and would move mechanically with $b$ if the blocks were\n",
+    "ragged. Multiplying by\n",
+    "$N/b$ puts it back on the full-data scale of the negative evidence lower bound\n",
+    "(ELBO), and with equal blocks\n",
+    "that is one constant:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "ee19b8c7",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:12:51.296919Z",
+     "iopub.status.busy": "2026-08-18T19:12:51.296795Z",
+     "iopub.status.idle": "2026-08-18T19:12:51.632724Z",
+     "shell.execute_reply": "2026-08-18T19:12:51.631567Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "recorded loss over the two passes either side of the cut:\n",
+      "  spread (sd): 13,192 before, 13,093 after\n",
+      "  level (mean): -96,528 before, -97,746 after\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x320 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 341,
+       "width": 914
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "ELBO_SCALE = loader.total_size / BATCH  # undo PyMC's scale_cost_to_minibatch\n",
+    "loss = np.asarray(approx.hist, dtype=float) * ELBO_SCALE\n",
+    "\n",
+    "epoch_mean = np.convolve(loss, np.ones(steps_per_epoch) / steps_per_epoch, mode=\"valid\")\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 3.2), layout=\"constrained\")\n",
+    "ax.plot(loss, lw=0.5, alpha=0.6, label=\"per step\")\n",
+    "ax.plot(\n",
+    "    np.arange(steps_per_epoch - 1, len(loss)),\n",
+    "    epoch_mean,\n",
+    "    color=\"C1\",\n",
+    "    lw=1.5,\n",
+    "    label=\"one-epoch moving mean\",\n",
+    ")\n",
+    "ax.axvline(6_000, color=\"k\", ls=\"--\", lw=1, label=\"learning rate cut, 0.02 to 0.005\")\n",
+    "# clip to the plateau: the first few hundred steps are orders of magnitude higher\n",
+    "plateau = loss[1_000:]\n",
+    "ax.set_ylim(np.quantile(plateau, 0.001), np.quantile(plateau, 0.999))\n",
+    "ax.yaxis.set_major_formatter(StrMethodFormatter(\"{x:,.0f}\"))\n",
+    "ax.set_xlabel(\"step\")\n",
+    "ax.set_ylabel(\"negative ELBO, full-data scale\")\n",
+    "ax.set_title(\"Streaming ADVI loss on the plateau (steps before 1,000 are off scale)\")\n",
+    "ax.legend(loc=\"upper right\", fontsize=9, frameon=True)\n",
+    "before, after = loss[6_000 - 2 * steps_per_epoch : 6_000], loss[6_000 : 6_000 + 2 * steps_per_epoch]\n",
+    "print(\n",
+    "    \"recorded loss over the two passes either side of the cut:\\n\"\n",
+    "    f\"  spread (sd): {before.std():,.0f} before, {after.std():,.0f} after\\n\"\n",
+    "    f\"  level (mean): {before.mean():,.0f} before, {after.mean():,.0f} after\"\n",
+    ");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c369e720",
+   "metadata": {},
+   "source": [
+    "## The last iterate is not the answer\n",
+    "\n",
+    "Before reading a single parameter off this fit, one property of the replay has\n",
+    "to be dealt with. The batch at step $t$ is a deterministic function of $t$ with\n",
+    "period one epoch, so the data term of every stochastic gradient repeats with\n",
+    "that period. The parameters and the Monte Carlo draw do not, so the realized\n",
+    "gradients are not literally periodic, but the iterate inherits a component\n",
+    "locked to the replay order. Two measurements separate that component from\n",
+    "genuine progress: compare the same phase in consecutive epochs, and look at the\n",
+    "spread within a single epoch."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "07d51dca",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:12:51.635695Z",
+     "iopub.status.busy": "2026-08-18T19:12:51.635449Z",
+     "iopub.status.idle": "2026-08-18T19:12:51.650726Z",
+     "shell.execute_reply": "2026-08-18T19:12:51.649881Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "same phase, epoch to epoch, max over coordinates per pair:\n",
+      "  6.37 2.93 2.59 0.75 1.39 1.45 0.58\n",
+      "  median over coordinates, last pair: 0.11 sd\n",
+      "within the last epoch: median 4.31, max 41.2 sd\n"
+     ]
+    }
+   ],
+   "source": [
+    "mu_t = np.asarray(tail.mu)\n",
+    "sd_unc = approx.std.eval()  # variational sd in the unconstrained space, before averaging\n",
+    "ends = mu_t[steps_per_epoch - 1 :: steps_per_epoch]  # same phase, one epoch apart\n",
+    "step = np.abs(np.diff(ends, axis=0)) / sd_unc  # (7 epoch pairs, 154 coordinates)\n",
+    "last = mu_t[-steps_per_epoch:]\n",
+    "swing = (last.max(0) - last.min(0)) / sd_unc\n",
+    "print(\n",
+    "    \"same phase, epoch to epoch, max over coordinates per pair:\\n  \"\n",
+    "    + \" \".join(f\"{v:.2f}\" for v in step.max(1))\n",
+    "    + f\"\\n  median over coordinates, last pair: {np.median(step[-1]):.2f} sd\"\n",
+    "    f\"\\nwithin the last epoch: median {np.median(swing):.2f}, max {swing.max():.1f} sd\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1152a3c1",
+   "metadata": {},
+   "source": [
+    "Between the same phase of consecutive epochs the largest movement decays from about six posterior widths\n",
+    "in the first pair to about half a width in the last, and the median coordinate\n",
+    "moves a tenth of a width per pass; within a single epoch the same coordinates\n",
+    "swing by multiples of their width. So the optimizer is not converging to a\n",
+    "point. It is orbiting one, on a cycle locked to the order the rows are replayed\n",
+    "in, and most of where the last iterate sits is the phase of that orbit at the\n",
+    "step the budget ran out.\n",
+    "\n",
+    "The number you would report therefore depends on where you stop, so the fit\n",
+    "is summarized by averaging the variational parameters over the final whole\n",
+    "pass. To the extent the order-locked\n",
+    "component repeats from one pass to the next, a window of exactly one pass\n",
+    "averages it out (which is why the step budget above is a whole number of\n",
+    "epochs), and a window that is not a whole number of passes leaves a residual of\n",
+    "the order of one swing divided by the window length. This is Polyak–Ruppert averaging {cite:p}`polyak1992acceleration`. What the\n",
+    "average does *not* remove\n",
+    "is the slow drift the first line still shows: a tenth of a width per pass in\n",
+    "the median, and much more along the directions the recovery table is about to\n",
+    "single out."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "34d17006",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:12:51.652283Z",
+     "iopub.status.busy": "2026-08-18T19:12:51.652164Z",
+     "iopub.status.idle": "2026-08-18T19:12:51.655970Z",
+     "shell.execute_reply": "2026-08-18T19:12:51.655304Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "variational parameters averaged over the final 300 steps (one epoch)\n"
+     ]
+    }
+   ],
+   "source": [
+    "rho_t = np.asarray(tail.rho)\n",
+    "approx.params[0].set_value(mu_t[-steps_per_epoch:].mean(0))\n",
+    "approx.params[1].set_value(rho_t[-steps_per_epoch:].mean(0))\n",
+    "print(f\"variational parameters averaged over the final {steps_per_epoch} steps (one epoch)\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d19f1039",
+   "metadata": {},
+   "source": [
+    "Everything below reads from that averaged approximation."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "26757243",
+   "metadata": {},
+   "source": [
+    "## Did it recover the truth?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "4d51fcc9",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:12:51.657876Z",
+     "iopub.status.busy": "2026-08-18T19:12:51.657765Z",
+     "iopub.status.idle": "2026-08-18T19:12:51.871295Z",
+     "shell.execute_reply": "2026-08-18T19:12:51.870626Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>mean</th>\n",
+       "      <th>sd</th>\n",
+       "      <th>truth</th>\n",
+       "      <th>abs_error</th>\n",
+       "      <th>z</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>kappa0</th>\n",
+       "      <td>0.5336</td>\n",
+       "      <td>0.0048</td>\n",
+       "      <td>0.8473</td>\n",
+       "      <td>0.3137</td>\n",
+       "      <td>-65.2181</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alpha0</th>\n",
+       "      <td>-1.7012</td>\n",
+       "      <td>0.0034</td>\n",
+       "      <td>-2.9957</td>\n",
+       "      <td>1.2946</td>\n",
+       "      <td>378.5027</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>lambda_a</th>\n",
+       "      <td>0.3517</td>\n",
+       "      <td>0.0053</td>\n",
+       "      <td>0.3500</td>\n",
+       "      <td>0.0017</td>\n",
+       "      <td>0.3298</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>lambda_q</th>\n",
+       "      <td>0.2065</td>\n",
+       "      <td>0.0056</td>\n",
+       "      <td>0.2000</td>\n",
+       "      <td>0.0065</td>\n",
+       "      <td>1.1587</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_a</th>\n",
+       "      <td>0.1624</td>\n",
+       "      <td>0.0036</td>\n",
+       "      <td>0.1800</td>\n",
+       "      <td>0.0176</td>\n",
+       "      <td>-4.8789</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_q</th>\n",
+       "      <td>0.1223</td>\n",
+       "      <td>0.0031</td>\n",
+       "      <td>0.1200</td>\n",
+       "      <td>0.0023</td>\n",
+       "      <td>0.7391</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>theta_d</th>\n",
+       "      <td>0.0200</td>\n",
+       "      <td>0.0002</td>\n",
+       "      <td>0.0200</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>-0.0473</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>theta_r</th>\n",
+       "      <td>0.2500</td>\n",
+       "      <td>0.0019</td>\n",
+       "      <td>0.2500</td>\n",
+       "      <td>0.0000</td>\n",
+       "      <td>0.0151</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>nu</th>\n",
+       "      <td>3.5097</td>\n",
+       "      <td>0.0230</td>\n",
+       "      <td>3.5000</td>\n",
+       "      <td>0.0097</td>\n",
+       "      <td>0.4224</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>kappa0 + mean(b_k)</th>\n",
+       "      <td>0.8495</td>\n",
+       "      <td>0.0096</td>\n",
+       "      <td>0.8473</td>\n",
+       "      <td>0.0022</td>\n",
+       "      <td>0.2253</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>alpha0 + mean(b_al)</th>\n",
+       "      <td>-2.9948</td>\n",
+       "      <td>0.0064</td>\n",
+       "      <td>-2.9957</td>\n",
+       "      <td>0.0009</td>\n",
+       "      <td>0.1427</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>beta_a + mean(b_ba)</th>\n",
+       "      <td>0.1813</td>\n",
+       "      <td>0.0057</td>\n",
+       "      <td>0.1800</td>\n",
+       "      <td>0.0013</td>\n",
+       "      <td>0.2236</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                       mean      sd   truth  abs_error         z\n",
+       "kappa0               0.5336  0.0048  0.8473     0.3137  -65.2181\n",
+       "alpha0              -1.7012  0.0034 -2.9957     1.2946  378.5027\n",
+       "lambda_a             0.3517  0.0053  0.3500     0.0017    0.3298\n",
+       "lambda_q             0.2065  0.0056  0.2000     0.0065    1.1587\n",
+       "beta_a               0.1624  0.0036  0.1800     0.0176   -4.8789\n",
+       "beta_q               0.1223  0.0031  0.1200     0.0023    0.7391\n",
+       "theta_d              0.0200  0.0002  0.0200     0.0000   -0.0473\n",
+       "theta_r              0.2500  0.0019  0.2500     0.0000    0.0151\n",
+       "nu                   3.5097  0.0230  3.5000     0.0097    0.4224\n",
+       "kappa0 + mean(b_k)   0.8495  0.0096  0.8473     0.0022    0.2253\n",
+       "alpha0 + mean(b_al) -2.9948  0.0064 -2.9957     0.0009    0.1427\n",
+       "beta_a + mean(b_ba)  0.1813  0.0057  0.1800     0.0013    0.2236"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "idata = approx.sample(2_000, random_seed=RANDOM_SEED)\n",
+    "post = idata.posterior\n",
+    "\n",
+    "scalar_params = [\n",
+    "    \"kappa0\",\n",
+    "    \"alpha0\",\n",
+    "    \"lambda_a\",\n",
+    "    \"lambda_q\",\n",
+    "    \"beta_a\",\n",
+    "    \"beta_q\",\n",
+    "    \"theta_d\",\n",
+    "    \"theta_r\",\n",
+    "    \"nu\",\n",
+    "]\n",
+    "rows = {name: [float(post[name].mean()), float(post[name].std())] for name in scalar_params}\n",
+    "# the intercepts share a ridge with their group means; only the sums are identified\n",
+    "for intercept, b in [(\"kappa0\", \"b_k\"), (\"alpha0\", \"b_al\"), (\"beta_a\", \"b_ba\")]:\n",
+    "    s = post[intercept] + post[b].mean(\"symbol\")\n",
+    "    rows[f\"{intercept} + mean({b})\"] = [float(s.mean()), float(s.std())]\n",
+    "recovery = pd.DataFrame(rows, index=[\"mean\", \"sd\"]).T\n",
+    "recovery[\"truth\"] = [truth[p] for p in scalar_params] + [\n",
+    "    truth[\"kappa0\"],\n",
+    "    truth[\"alpha0\"],\n",
+    "    truth[\"beta_a\"],\n",
+    "]\n",
+    "recovery[\"abs_error\"] = (recovery[\"mean\"] - recovery[\"truth\"]).abs()\n",
+    "recovery[\"z\"] = (recovery[\"mean\"] - recovery[\"truth\"]) / recovery[\"sd\"]\n",
+    "recovery.round(4)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "f0630464",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:12:51.873195Z",
+     "iopub.status.busy": "2026-08-18T19:12:51.872946Z",
+     "iopub.status.idle": "2026-08-18T19:13:09.175023Z",
+     "shell.execute_reply": "2026-08-18T19:13:09.174557Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "identified rows, max |z| against the truth, each fit in its own posterior sd:\n",
+      "  last iterate: 3.42\n",
+      "  seed 0: 1.16\n",
+      "  seed 1, same order: 1.14\n",
+      "  seed 2, other order: 1.13\n",
+      "spread of the mean across the three tail-averaged fits, in seed-0 sd:\n",
+      "  median 0.08, max 0.10\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Is the table a property of the problem or of one optimizer trajectory? Three\n",
+    "# more readings of the same data and budget: the notebook's own last iterate\n",
+    "# before averaging; a refit with a different optimizer seed on the same replay\n",
+    "# order; and a refit on a different on-disk order — the replay order is the one\n",
+    "# ingredient the orbit story is about, so it is the one worth varying.\n",
+    "raw_splits = [\"kappa0\", \"alpha0\", \"beta_a\"]\n",
+    "identified = [name for name in recovery.index if name not in raw_splits]\n",
+    "\n",
+    "\n",
+    "def summarize(post_):\n",
+    "    \"\"\"Posterior mean and sd of every row the recovery table reports.\"\"\"\n",
+    "    out = {name: (float(post_[name].mean()), float(post_[name].std())) for name in scalar_params}\n",
+    "    for intercept, b in [(\"kappa0\", \"b_k\"), (\"alpha0\", \"b_al\"), (\"beta_a\", \"b_ba\")]:\n",
+    "        total = post_[intercept] + post_[b].mean(\"symbol\")\n",
+    "        out[f\"{intercept} + mean({b})\"] = (float(total.mean()), float(total.std()))\n",
+    "    return pd.DataFrame(out, index=[\"mean\", \"sd\"]).T\n",
+    "\n",
+    "\n",
+    "def reshuffle(src_dir, salt):\n",
+    "    \"\"\"A second on-disk order: re-key the already-shuffled rows with a different salt.\"\"\"\n",
+    "    dst_dir = tempfile.mkdtemp(prefix=\"ticks_reorder_\")\n",
+    "    full = pa.concat_tables(\n",
+    "        [pq.read_table(os.path.join(src_dir, f)) for f in sorted(os.listdir(src_dir))]\n",
+    "    )\n",
+    "    order2 = np.argsort(\n",
+    "        splitmix64(np.arange(full.num_rows, dtype=np.uint64) + np.uint64(salt)), kind=\"stable\"\n",
+    "    )\n",
+    "    for i in range(n_shards):\n",
+    "        pq.write_table(\n",
+    "            full.take(order2[i::n_shards]),\n",
+    "            os.path.join(dst_dir, f\"shard_{i:03d}.parquet\"),\n",
+    "            row_group_size=BATCH,\n",
+    "        )\n",
+    "    return dst_dir\n",
+    "\n",
+    "\n",
+    "def refit(seed, data_loader):\n",
+    "    stream_s, tail_s = StreamAdvance(model, data_loader), ParamTrace()\n",
+    "    stream_s.prime()\n",
+    "    with model:\n",
+    "        advi_s = pm.ADVI(random_seed=seed)\n",
+    "    advi_s.fit(\n",
+    "        6_000, obj_optimizer=pm.adam(learning_rate=0.02), callbacks=[stream_s], progressbar=False\n",
+    "    )\n",
+    "    approx_s = advi_s.fit(\n",
+    "        2_400,\n",
+    "        obj_optimizer=pm.adam(learning_rate=0.005),\n",
+    "        callbacks=[stream_s, tail_s],\n",
+    "        progressbar=False,\n",
+    "    )\n",
+    "    approx_s.params[0].set_value(np.asarray(tail_s.mu)[-steps_per_epoch:].mean(0))\n",
+    "    approx_s.params[1].set_value(np.asarray(tail_s.rho)[-steps_per_epoch:].mean(0))\n",
+    "    return summarize(approx_s.sample(2_000, random_seed=seed).posterior)\n",
+    "\n",
+    "\n",
+    "approx.params[0].set_value(mu_t[-1])\n",
+    "approx.params[1].set_value(rho_t[-1])\n",
+    "last_iterate = summarize(approx.sample(2_000, random_seed=RANDOM_SEED).posterior)\n",
+    "approx.params[0].set_value(mu_t[-steps_per_epoch:].mean(0))\n",
+    "approx.params[1].set_value(rho_t[-steps_per_epoch:].mean(0))\n",
+    "\n",
+    "other_dir = reshuffle(data_dir, salt=1)\n",
+    "other_loader = DataLoader(\n",
+    "    parquet_source(other_dir, columns=columns), batch_size=BATCH, shuffle=False, total_size=\"auto\"\n",
+    ")\n",
+    "readings = {\n",
+    "    \"last iterate\": last_iterate,\n",
+    "    \"seed 0\": recovery[[\"mean\", \"sd\"]],\n",
+    "    \"seed 1, same order\": refit(RANDOM_SEED + 1, loader),\n",
+    "    \"seed 2, other order\": refit(RANDOM_SEED + 2, other_loader),\n",
+    "}\n",
+    "tru = recovery.loc[identified, \"truth\"]\n",
+    "z_own = {\n",
+    "    k: ((v.loc[identified, \"mean\"] - tru) / v.loc[identified, \"sd\"]).abs().max()\n",
+    "    for k, v in readings.items()\n",
+    "}\n",
+    "means = pd.DataFrame(\n",
+    "    {k: v.loc[identified, \"mean\"] for k, v in readings.items() if k != \"last iterate\"}\n",
+    ")\n",
+    "spread = means.std(axis=1) / recovery.loc[identified, \"sd\"]\n",
+    "print(\n",
+    "    \"identified rows, max |z| against the truth, each fit in its own posterior sd:\\n  \"\n",
+    "    + \"\\n  \".join(f\"{k}: {v:.2f}\" for k, v in z_own.items())\n",
+    "    + \"\\nspread of the mean across the three tail-averaged fits, in seed-0 sd:\"\n",
+    "    f\"\\n  median {spread.median():.2f}, max {spread.max():.2f}\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3b0c9524",
+   "metadata": {},
+   "source": [
+    "The table is a selection, the scalar globals and the three identified sums,\n",
+    "and the figure after it checks one of the five symbol-effect families; that,\n",
+    "plus the two curves for one symbol further down, is the extent of the recovery\n",
+    "evidence here. Read the table bottom-up. The raw intercepts look off by 0.3\n",
+    "and 1.3, but a global coefficient and the mean of its group effects are only *jointly*\n",
+    "pinned by the likelihood, which is exactly flat along\n",
+    "$(\\alpha_0 + d,\\ b^{(\\alpha)} - d)$. What tilts that direction at all is the\n",
+    "hierarchical prior: shifting every symbol effect by $d$ costs\n",
+    "$\\sum_s (b_s - d)^2 / 2\\tau^2$, which is minimized when the effects average\n",
+    "to zero, and the generator standardized them to average exactly zero, so the\n",
+    "prior points the split at the generating value. Conditional on everything\n",
+    "else, that cost is a Gaussian in $d$ with standard deviation $\\tau/\\sqrt{12}$,\n",
+    "a tenth for $\\tau$ near the generator's $0.35$, which is the scale of\n",
+    "uncertainty the split actually carries. The same\n",
+    "translation ridge exists for every global-coefficient/group-effect pair in\n",
+    "this model (including the vector pairs $c$/$b^{(\\pi h)}$ and\n",
+    "$g$/$b^{(\\sigma h)}$, whose sums the table omits).\n",
+    "\n",
+    "Two things follow, and the table shows both. Along a direction that flat, a\n",
+    "gradient optimizer crawls: 8,400 steps have carried `alpha0` to $-1.70$ on its\n",
+    "way to $-3.00$. To see that it is still traveling, warm-start a fresh optimizer\n",
+    "at the last iterate and give it sixty more passes at the same learning rate:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "ef1403c5",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:13:09.176608Z",
+     "iopub.status.busy": "2026-08-18T19:13:09.176497Z",
+     "iopub.status.idle": "2026-08-18T19:13:21.319806Z",
+     "shell.execute_reply": "2026-08-18T19:13:21.319322Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "after +0 / +30 / +60 passes\n",
+      "  kappa0: 0.533 / 0.579 / 0.628 (generating 0.847)\n",
+      "  alpha0: -1.702 / -1.711 / -1.722 (generating -2.996)\n",
+      "  loss per pass over those 60: -0.37 nats\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Continue from the last iterate on a fresh optimizer, so `approx` and its loss\n",
+    "# history above are left exactly as they were.\n",
+    "with model:\n",
+    "    advi_more = pm.ADVI(random_seed=RANDOM_SEED)\n",
+    "advi_more.approx.params[0].set_value(mu_t[-1])\n",
+    "advi_more.approx.params[1].set_value(rho_t[-1])\n",
+    "stream_more = StreamAdvance(model, loader)\n",
+    "stream_more.prime()\n",
+    "where = {name: approx.ordering[name][1].start for name in [\"kappa0\", \"alpha0\"]}\n",
+    "path = [[mu_t[-1][where[\"kappa0\"]], mu_t[-1][where[\"alpha0\"]]]]  # +0 passes\n",
+    "\n",
+    "\n",
+    "class EpochMeans:\n",
+    "    def __call__(self, approx_, losses, i):\n",
+    "        if i % steps_per_epoch == 0:\n",
+    "            mu_now = approx_.params[0].get_value()\n",
+    "            path.append([mu_now[where[\"kappa0\"]], mu_now[where[\"alpha0\"]]])\n",
+    "\n",
+    "\n",
+    "advi_more.fit(\n",
+    "    60 * steps_per_epoch,\n",
+    "    obj_optimizer=pm.adam(learning_rate=0.005),\n",
+    "    callbacks=[stream_more, EpochMeans()],\n",
+    "    progressbar=False,\n",
+    ")\n",
+    "path = np.asarray(path)\n",
+    "more_loss = np.asarray(advi_more.hist) * ELBO_SCALE\n",
+    "print(\n",
+    "    \"after +0 / +30 / +60 passes\\n\"\n",
+    "    f\"  kappa0: {' / '.join(f'{v:.3f}' for v in path[[0, 30, 60], 0])} (generating {truth['kappa0']:.3f})\\n\"\n",
+    "    f\"  alpha0: {' / '.join(f'{v:.3f}' for v in path[[0, 30, 60], 1])} (generating {truth['alpha0']:.3f})\\n\"\n",
+    "    \"  loss per pass over those 60: \"\n",
+    "    f\"{(more_loss[-steps_per_epoch:].mean() - more_loss[:steps_per_epoch].mean()) / 59:+.2f} nats\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "c8381980",
+   "metadata": {},
+   "source": [
+    "The ridge coordinates keep moving toward their generating values, at a rate the\n",
+    "loss barely registers. The raw split rows are a fit still traveling along a\n",
+    "nearly flat direction, not an ambiguity in the model. And their reported\n",
+    "widths, a few thousandths, are the mean-field *conditional* width across the ridge, not the tenth just\n",
+    "computed along it: mean-field has no correlation to spend,\n",
+    "so it reports the narrow direction as if it were the wide one, which is why\n",
+    "`alpha0` reads $z = 379$. Both are reasons a sum-to-zero constraint on the\n",
+    "symbol effects is the standard reparameterization when the split itself\n",
+    "matters: it removes the direction instead of asking the optimizer to find its\n",
+    "way along it.\n",
+    "\n",
+    "The identified rows are a different story. Every row the table reports that\n",
+    "*is* pinned by the likelihood, the three sums and the six standalone\n",
+    "coefficients, lands inside $1.2$ posterior standard deviations of its\n",
+    "generating value, and seven of those nine are inside $0.5$. The replicate cell puts that in context from two directions.\n",
+    "Read from the last iterate instead of the tail average, the same rows ran to\n",
+    "$3.4$; that difference was the orbit, not the estimate. And on two refits,\n",
+    "one with a different optimizer seed on the same replay order and one on a\n",
+    "different on-disk order altogether, the identified rows land in the same\n",
+    "range, each fit judged in its own posterior width, with the spread of the means\n",
+    "across the three tail-averaged fits about a tenth of the reported width. That\n",
+    "is the replication check, and what it buys is limited. Material disagreement\n",
+    "between fits would have been enough to withhold any claim about a width;\n",
+    "agreement only removes the instability warning. It does not validate\n",
+    "calibration. The widths here are those of a mean-field approximation, and\n",
+    "z-scores this small on one dataset are consistent with widths that are\n",
+    "somewhat too narrow, somewhat too wide, or right; sizing that needs many\n",
+    "simulated datasets, and this notebook fits one.\n",
+    "\n",
+    "The more interesting check is hierarchical: what happened to the per-symbol\n",
+    "effects of the two symbols with 1,800 and 900 rows?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "id": "dddf201a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:13:21.321755Z",
+     "iopub.status.busy": "2026-08-18T19:13:21.321645Z",
+     "iopub.status.idle": "2026-08-18T19:13:21.482179Z",
+     "shell.execute_reply": "2026-08-18T19:13:21.481799Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "posterior sd of the contrast, widest first:\n",
+      "  S11 0.044 (900 rows), S10 0.023 (1,800 rows), S09 0.019 (4,600 rows)\n",
+      "  ... narrowest S00 0.007 (90,700 rows)\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 361,
+       "width": 886
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# plot the identified CONTRAST b - mean(b): the table above showed the absolute\n",
+    "# level belongs to a ridge with the intercept, so comparing raw b to raw truth\n",
+    "# would only display the arbitrary level split. Hand-rolled rather than\n",
+    "# az.plot_forest because the display is bespoke: truth overlay + thin shading.\n",
+    "bc = post[\"b_al\"] - post[\"b_al\"].mean(\"symbol\")\n",
+    "b_mean = bc.mean((\"chain\", \"draw\")).values\n",
+    "b_sd = bc.std((\"chain\", \"draw\")).values\n",
+    "b_truth = 0.35 * z_truth[\"z_al\"]  # the generator's scale times its z draws\n",
+    "b_truth = b_truth - b_truth.mean()\n",
+    "\n",
+    "fig, ax = plt.subplots(figsize=(8, 3.5), layout=\"constrained\")\n",
+    "x = np.arange(n_symbols)\n",
+    "ax.errorbar(\n",
+    "    x,\n",
+    "    b_mean,\n",
+    "    yerr=2 * b_sd,\n",
+    "    fmt=\"o\",\n",
+    "    color=\"C0\",\n",
+    "    capsize=3,\n",
+    "    label=\"mean ± 2 posterior sd (mean-field)\",\n",
+    ")\n",
+    "ax.scatter(x, b_truth, marker=\"x\", color=\"C1\", s=60, zorder=3, label=\"truth\")\n",
+    "for t in thin:\n",
+    "    ax.axvspan(t - 0.4, t + 0.4, color=\"C3\", alpha=0.12)\n",
+    "ax.set_xticks(x)\n",
+    "ax.set_xticklabels([f\"S{i}\" for i in x])\n",
+    "ax.set_xlabel(\"symbol (shaded = thin: 1,800 and 900 rows)\")\n",
+    "ax.set_ylabel(r\"$b^{(\\alpha)}_s - \\bar{b}^{(\\alpha)}$\")\n",
+    "ax.set_title(\"Symbol effects against the generating values (thin symbols shaded)\")\n",
+    "ax.legend()\n",
+    "\n",
+    "order = np.argsort(-b_sd)\n",
+    "print(\n",
+    "    \"posterior sd of the contrast, widest first:\\n  \"\n",
+    "    + \", \".join(f\"S{i:02d} {b_sd[i]:.3f} ({counts[i]:,} rows)\" for i in order[:3])\n",
+    "    + f\"\\n  ... narrowest S{order[-1]:02d} {b_sd[order[-1]]:.3f} ({counts[order[-1]]:,} rows)\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b9c4eb60",
+   "metadata": {},
+   "source": [
+    "The posterior spread is widest for the symbols with the least data (the\n",
+    "printout orders them), which is the hierarchy expressing that it\n",
+    "knows less about those groups {cite:p}`gelman2006data`. The point estimates\n",
+    "track their generating values across the board, including the thin ones.\n",
+    "\n",
+    "What the figure does *not* show is shrinkage in the strict sense: that would\n",
+    "need an unpooled per-symbol fit to compare against, and this notebook does not\n",
+    "run one. Nor are the bars calibrated intervals; the paragraph above declined\n",
+    "to treat mean-field widths that way, and that applies to their ordering too.\n",
+    "And whether pooling improves held-out prediction on real data is a separate\n",
+    "question again, which nothing here tests.\n",
+    "\n",
+    "What do those parameters mean as *objects*? For one symbol, holding the\n",
+    "covariates at $a = q = 0$, the fit implies two curves over the event clock: the\n",
+    "probability that the next event leaves the price unchanged, and the 90%\n",
+    "half-width of the move given that one happens. Both are built from\n",
+    "global-plus-symbol sums, so neither sits on the translation ridge."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "id": "af541d05",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:13:21.483708Z",
+     "iopub.status.busy": "2026-08-18T19:13:21.483603Z",
+     "iopub.status.idle": "2026-08-18T19:13:21.632988Z",
+     "shell.execute_reply": "2026-08-18T19:13:21.632449Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 900x320 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 337,
+       "width": 911
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "s_idx, hours = 0, np.arange(24)\n",
+    "Bh = hour_basis(hours)  # (24, 4)\n",
+    "# .dataset: idata.posterior is a DataTree in arviz 1.x\n",
+    "st = post.dataset.stack(sample=(\"chain\", \"draw\"))\n",
+    "\n",
+    "\n",
+    "def pick(name, *dims):\n",
+    "    return st[name].transpose(*dims, \"sample\").values\n",
+    "\n",
+    "\n",
+    "logit_pi = (\n",
+    "    pick(\"kappa0\")[None, :]\n",
+    "    + pick(\"b_k\", \"symbol\")[s_idx][None, :]\n",
+    "    + Bh @ (pick(\"c\", \"harmonic\") + pick(\"b_ph\", \"symbol\", \"harmonic\")[s_idx])\n",
+    ")\n",
+    "log_sigma = (\n",
+    "    pick(\"alpha0\")[None, :]\n",
+    "    + pick(\"b_al\", \"symbol\")[s_idx][None, :]\n",
+    "    + Bh @ (pick(\"g\", \"harmonic\") + pick(\"b_sh\", \"symbol\", \"harmonic\")[s_idx])\n",
+    ")\n",
+    "# transform each draw, then summarize: the median of a function, not a function of medians\n",
+    "zero_prob = np.median(1.0 / (1.0 + np.exp(logit_pi)), axis=1)\n",
+    "half90 = np.median(np.exp(log_sigma) * stats.t.ppf(0.95, pick(\"nu\"))[None, :], axis=1)\n",
+    "\n",
+    "t_logit = (\n",
+    "    truth[\"kappa0\"]\n",
+    "    + 0.30 * z_truth[\"z_k\"][s_idx]\n",
+    "    + Bh @ (truth[\"c\"] + 0.15 * z_truth[\"z_ph\"][s_idx])\n",
+    ")\n",
+    "t_logsig = (\n",
+    "    truth[\"alpha0\"]\n",
+    "    + 0.35 * z_truth[\"z_al\"][s_idx]\n",
+    "    + Bh @ (truth[\"g\"] + 0.12 * z_truth[\"z_sh\"][s_idx])\n",
+    ")\n",
+    "\n",
+    "fig, axs = plt.subplots(1, 2, figsize=(9, 3.2), layout=\"constrained\")\n",
+    "for ax, post_curve, truth_curve, ylab in [\n",
+    "    (axs[0], zero_prob, 1.0 / (1.0 + np.exp(t_logit)), \"P(next event does not move)\"),\n",
+    "    (axs[1], half90, np.exp(t_logsig) * stats.t.ppf(0.95, truth[\"nu\"]), \"90% half-width (bp)\"),\n",
+    "]:\n",
+    "    ax.plot(hours, post_curve, color=\"C0\", lw=2, label=\"posterior median\")\n",
+    "    ax.plot(hours, truth_curve, color=\"C1\", ls=\"--\", lw=1.5, label=\"generator truth\")\n",
+    "    ax.set_xlabel(\"UTC hour\")\n",
+    "    ax.set_ylabel(ylab)\n",
+    "axs[0].legend(fontsize=9)\n",
+    "fig.suptitle(f\"SYM{s_idx:02d} on the event clock, at a = q = 0\", fontsize=11);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3a858983",
+   "metadata": {},
+   "source": [
+    "The half-width is a quantile of the *outcome* distribution, not a credible band\n",
+    "for the curve: it answers \"how far does a move go\", and it stays finite for any\n",
+    "$\\nu > 0$. That is why the notebook reports it instead of a conditional\n",
+    "standard deviation: the Student-t variance exists only for $\\nu > 2$, and\n",
+    "nothing in this model guarantees that. Under this generator, the fit recovers\n",
+    "both shapes; on real data the same two curves would be estimates, and would\n",
+    "need out-of-sample evaluation before being used for anything."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "025e4d72",
+   "metadata": {},
+   "source": [
+    "## In-sample posterior predictive adequacy\n",
+    "\n",
+    "A recovery table checks parameters; a posterior predictive check asks the\n",
+    "model to reproduce the data features it exists to describe. The `CustomDist`\n",
+    "carries a `random` implementation alongside its `logp`, so\n",
+    "{func}`~pymc.sample_posterior_predictive` works out of the box. The two\n",
+    "features that matter for this model are the exact-zero share and the heavy\n",
+    "conditional tail; the figure after the table shows the whole empirical\n",
+    "cumulative distribution function (ECDF) of one batch against sixty predictive\n",
+    "draws:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "id": "cd43c932",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:13:21.634373Z",
+     "iopub.status.busy": "2026-08-18T19:13:21.634263Z",
+     "iopub.status.idle": "2026-08-18T19:13:22.564992Z",
+     "shell.execute_reply": "2026-08-18T19:13:22.564552Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>observed</th>\n",
+       "      <th>predictive mean</th>\n",
+       "      <th>predictive 5%</th>\n",
+       "      <th>predictive 95%</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>zero share</th>\n",
+       "      <td>0.300</td>\n",
+       "      <td>0.322</td>\n",
+       "      <td>0.299</td>\n",
+       "      <td>0.346</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>median |move| (bp)</th>\n",
+       "      <td>0.039</td>\n",
+       "      <td>0.040</td>\n",
+       "      <td>0.037</td>\n",
+       "      <td>0.043</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>q99 |move| (bp)</th>\n",
+       "      <td>0.298</td>\n",
+       "      <td>0.331</td>\n",
+       "      <td>0.268</td>\n",
+       "      <td>0.410</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                    observed  predictive mean  predictive 5%  predictive 95%\n",
+       "zero share             0.300            0.322          0.299           0.346\n",
+       "median |move| (bp)     0.039            0.040          0.037           0.043\n",
+       "q99 |move| (bp)        0.298            0.331          0.268           0.410"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eval_batch = next(iter(loader))\n",
+    "model[\"batch\"].set_value(eval_batch, borrow=True)  # same path the callback uses\n",
+    "with model:\n",
+    "    idata = pm.sample_posterior_predictive(\n",
+    "        idata,\n",
+    "        var_names=[\"y_obs\"],\n",
+    "        random_seed=RANDOM_SEED,\n",
+    "        extend_inferencedata=True,\n",
+    "        progressbar=False,\n",
+    "    )\n",
+    "\n",
+    "pp = idata.posterior_predictive[\"y_obs\"].stack(sample=(\"chain\", \"draw\")).values.T\n",
+    "y_eval = eval_batch[:, 0]\n",
+    "\n",
+    "zero_share = (pp == 0).mean(axis=1)\n",
+    "med_nonzero = np.array([np.median(np.abs(d[d != 0])) for d in pp[:500]])\n",
+    "q99_nonzero = np.array([np.quantile(np.abs(d[d != 0]), 0.99) for d in pp[:500]])\n",
+    "\n",
+    "\n",
+    "def check_row(observed, draws):\n",
+    "    lo, hi = np.quantile(draws, [0.05, 0.95])\n",
+    "    return [observed, draws.mean(), lo, hi]\n",
+    "\n",
+    "\n",
+    "y_nonzero = np.abs(y_eval[y_eval != 0])\n",
+    "pd.DataFrame(\n",
+    "    [\n",
+    "        check_row((y_eval == 0).mean(), zero_share),\n",
+    "        check_row(np.median(y_nonzero), med_nonzero),\n",
+    "        check_row(np.quantile(y_nonzero, 0.99), q99_nonzero),\n",
+    "    ],\n",
+    "    columns=[\"observed\", \"predictive mean\", \"predictive 5%\", \"predictive 95%\"],\n",
+    "    index=[\"zero share\", \"median |move| (bp)\", \"q99 |move| (bp)\"],\n",
+    ").round(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "id": "d9992b9b",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:13:22.566172Z",
+     "iopub.status.busy": "2026-08-18T19:13:22.566077Z",
+     "iopub.status.idle": "2026-08-18T19:13:22.728412Z",
+     "shell.execute_reply": "2026-08-18T19:13:22.727895Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 800x350 with 1 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 361,
+       "width": 811
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# hand-rolled ECDF: the bespoke feature is the annotated discrete jump at zero,\n",
+    "# which the predictive must reproduce in both location and height\n",
+    "fig, ax = plt.subplots(figsize=(8, 3.5), layout=\"constrained\")\n",
+    "grid = np.linspace(-0.5, 0.5, 801)\n",
+    "for draw in pp[:60]:\n",
+    "    ax.plot(grid, np.searchsorted(np.sort(draw), grid) / draw.size, color=\"C0\", alpha=0.08, lw=1)\n",
+    "ax.plot(\n",
+    "    grid, np.searchsorted(np.sort(y_eval), grid) / y_eval.size, color=\"k\", lw=1.6, label=\"observed\"\n",
+    ")\n",
+    "ax.plot([], [], color=\"C0\", label=\"posterior predictive (60 draws)\")\n",
+    "zero_jump = (y_eval == 0).mean()\n",
+    "below = (y_eval < 0).mean()\n",
+    "ax.annotate(\n",
+    "    f\"vertical step at exactly 0:\\nthe zero share ({zero_jump:.0%})\",\n",
+    "    (0.03, below + zero_jump / 2),\n",
+    "    fontsize=9,\n",
+    "    ha=\"left\",\n",
+    "    va=\"center\",\n",
+    ")\n",
+    "ax.set_xlabel(\"next-event return (bp)\")\n",
+    "ax.set_ylabel(\"empirical CDF\")\n",
+    "ax.set_title(\"Posterior predictive ECDF: the step at zero is the hurdle\")\n",
+    "ax.legend(loc=\"upper left\");"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1141078c",
+   "metadata": {},
+   "source": [
+    "All three statistics sit inside their predictive 90% bands, though the zero\n",
+    "share sits close to the lower edge, 0.300 observed against a band starting at\n",
+    "0.299, so it is a pass with almost no margin rather than a comfortable one.\n",
+    "What that establishes is limited in two ways. The heading is literal: the\n",
+    "evaluation batch was seen during the fit, so this is adequacy, not\n",
+    "generalization, and a held-out split is the next thing to add before any of\n",
+    "this is used to compare models. And the check\n",
+    "is one batch, one step ahead, with the observed `ylag` held fixed: it does not\n",
+    "simulate a price path forward, and it says nothing about the hierarchy, the\n",
+    "hourly curves, or the covariate responses, each of which would need its own\n",
+    "stratified check."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "55abcdf1",
+   "metadata": {},
+   "source": [
+    "## What a stopping rule has to be able to see\n",
+    "\n",
+    "A streamed ELBO trace is noisy: every value is a one-batch, one-Monte Carlo\n",
+    "estimate, so consecutive losses differ mostly because the batch changed. That\n",
+    "makes \"has it converged?\" a signal-detection problem, and the horizon over\n",
+    "which you look is the whole game.\n",
+    "\n",
+    "Write the standardized *block contrast* at horizon $w$: average the $w$ losses\n",
+    "before a point, average the $w$ losses after it, and divide the difference by\n",
+    "the noise scale of that difference,\n",
+    "\n",
+    "$$\n",
+    "z_w(t) = \\frac{\\bar L_{t-2w:t-w} - \\bar L_{t-w:t}}\n",
+    "              {\\hat\\sigma \\sqrt{2/w}},\n",
+    "$$\n",
+    "\n",
+    "with $\\hat\\sigma$ estimated from successive differences. Positive $z_w$ means\n",
+    "the loss fell. For noise without long memory, averaging divides it by\n",
+    "$\\sqrt{w}$ while a steady drift accumulates linearly in $w$, so the detectable\n",
+    "drift shrinks like $w^{-3/2}$: the horizon does not merely smooth the picture,\n",
+    "it sets what is visible at all. A fixed replay order is not noise of that kind,\n",
+    "and the printout below shows where the generic scaling breaks, in the\n",
+    "notebook's favor."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "320fddcb",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:13:22.729856Z",
+     "iopub.status.busy": "2026-08-18T19:13:22.729752Z",
+     "iopub.status.idle": "2026-08-18T19:13:22.734093Z",
+     "shell.execute_reply": "2026-08-18T19:13:22.733656Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  per step (w=   1):  mean z -0.001   sd 0.99   |mean|/sd 0.00\n",
+      " one epoch (w= 300):  mean z +0.014   sd 0.03   |mean|/sd 0.56\n",
+      "two epochs (w= 600):  mean z +0.034   sd 0.02   |mean|/sd 2.06\n",
+      "1.5 epochs (w= 450):  mean z +0.021   sd 0.45   |mean|/sd 0.05\n",
+      "\n",
+      "control spread is 18x the widest aligned spread\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Stage 2 only, on the full-data scale: after the last planned optimizer change,\n",
+    "# which is where a stopping decision would actually be taken.\n",
+    "stage2 = loss[6_000:]\n",
+    "sigma_hat = np.mean(np.abs(np.diff(stage2))) * np.sqrt(np.pi) / 2.0\n",
+    "\n",
+    "\n",
+    "def signed_z(losses, w):\n",
+    "    \"\"\"Standardised contrast between adjacent blocks of w losses.\"\"\"\n",
+    "    csum = np.concatenate([[0.0], np.cumsum(losses)])\n",
+    "    t = np.arange(2 * w, len(losses))\n",
+    "    older = (csum[t - w] - csum[t - 2 * w]) / w\n",
+    "    newer = (csum[t] - csum[t - w]) / w\n",
+    "    return t, (older - newer) / (sigma_hat * np.sqrt(2.0 / w))\n",
+    "\n",
+    "\n",
+    "horizons = [(1, \"per step\"), (steps_per_epoch, \"one epoch\"), (2 * steps_per_epoch, \"two epochs\")]\n",
+    "# control: a horizon that is NOT a whole number of passes, to test whether the\n",
+    "# variance collapse below is really about epoch alignment\n",
+    "control = [(steps_per_epoch + steps_per_epoch // 2, \"1.5 epochs\")]\n",
+    "spreads = {}\n",
+    "for w, label in horizons + control:\n",
+    "    _, z = signed_z(stage2, w)\n",
+    "    spreads[label] = z.std()\n",
+    "    print(\n",
+    "        f\"{label:>10s} (w={w:4d}):  mean z {z.mean():+.3f}   sd {z.std():.2f}\"\n",
+    "        f\"   |mean|/sd {abs(z.mean()) / z.std():.2f}\"\n",
+    "    )\n",
+    "aligned = max(spreads[\"one epoch\"], spreads[\"two epochs\"])\n",
+    "print(f\"\\ncontrol spread is {spreads['1.5 epochs'] / aligned:.0f}x the widest aligned spread\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "id": "349f8ff2",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:13:22.735063Z",
+     "iopub.status.busy": "2026-08-18T19:13:22.734993Z",
+     "iopub.status.idle": "2026-08-18T19:13:23.003637Z",
+     "shell.execute_reply": "2026-08-18T19:13:23.003064Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 900x320 with 2 Axes>"
+      ]
+     },
+     "metadata": {
+      "image/png": {
+       "height": 331,
+       "width": 977
+      }
+     },
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, (ax, ax_zoom) = plt.subplots(\n",
+    "    1, 2, figsize=(9, 3.2), layout=\"constrained\", gridspec_kw={\"width_ratios\": [3, 2]}\n",
+    ")\n",
+    "for (w, label), color in zip(horizons, [\"C7\", \"C0\", \"C1\"]):\n",
+    "    t, z = signed_z(stage2, w)\n",
+    "    ax.plot(\n",
+    "        t,\n",
+    "        z,\n",
+    "        color=color,\n",
+    "        lw=0.9 if w == 1 else 1.6,\n",
+    "        alpha=0.5 if w == 1 else 1.0,\n",
+    "        label=f\"{label} (w={w})\",\n",
+    "    )\n",
+    "    if w > 1:\n",
+    "        ax_zoom.plot(t, z, color=color, lw=1.6, label=f\"{label} (w={w})\")\n",
+    "for ax_ in (ax, ax_zoom):\n",
+    "    ax_.axhline(0.0, color=\"k\", lw=0.8)\n",
+    "    ax_.set_xlabel(\"step within stage 2\")\n",
+    "ax.set_ylabel(r\"block contrast $z_w$\")\n",
+    "ax.set_title(\"Same trace, three horizons\", fontsize=11)\n",
+    "ax.legend(fontsize=8, loc=\"upper right\", frameon=True)\n",
+    "ax_zoom.set_ylim(-0.12, 0.12)\n",
+    "ax_zoom.set_title(\n",
+    "    f\"Aligned horizons only (control sd {spreads['1.5 epochs']:.2f} would fill this)\", fontsize=10\n",
+    ")\n",
+    "ax_zoom.legend(fontsize=8, loc=\"upper right\", frameon=True);"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "71cf8934",
+   "metadata": {},
+   "source": [
+    "Read the printout as three signal-to-noise ratios, and the figure as the same\n",
+    "thing twice: the left panel puts all three horizons on one scale, the right\n",
+    "panel zooms in on the two that are invisible at that scale. At $w = 1$ the\n",
+    "standardized contrast has unit spread (which is what the $\\sqrt{2/w}$ scaling\n",
+    "is built to produce) and a mean indistinguishable from zero: a single\n",
+    "increment says nothing about the trend.\n",
+    "\n",
+    "At the two epoch-aligned horizons the spread collapses far below what\n",
+    "independent noise would predict, and the control row explains why. The\n",
+    "shuffle is done once on disk and replayed in the same order every epoch, so a\n",
+    "window of exactly one or two passes averages over the *same rows* every time\n",
+    "and the batch-composition noise, which dominates the per-step view, all but\n",
+    "drops out.\n",
+    "Widen the window to one and a half passes and it does not: the printed ratio\n",
+    "puts the control an order of magnitude above the wider of its two neighbors,\n",
+    "even though it is itself *wider* than the one-epoch window. Nothing but the\n",
+    "alignment changed.\n",
+    "\n",
+    "What survives is Monte Carlo noise and the parameter drift\n",
+    "itself, and against that much smaller yardstick the drift becomes visible:\n",
+    "the ratio in the last column rises with the horizon. So, with a fixed replay\n",
+    "order, the useful horizons are the ones commensurate with an epoch. That is a\n",
+    "property of how the data was shuffled, not of the optimizer.\n",
+    "\n",
+    ":::{admonition} Why a naive rule fires instead of staying silent\n",
+    ":class: warning\n",
+    "It is tempting to accumulate per-step evidence with a one-sided cumulative-sum\n",
+    "statistic of the kind {cite:t}`page1954continuous` introduced, here adapted to\n",
+    "the standardized improvement: $S \\leftarrow \\max(0,\\ S + (\\kappa -\n",
+    "\\max(z, 0)))$, stopping when $S$ exceeds a threshold. Read carefully, that\n",
+    "recursion behaves sensibly while there is signal: if the standardized\n",
+    "improvement stays above $\\kappa$, the increment is negative and $S$ sits at\n",
+    "zero. The difficulty is what happens when there is *no* resolvable signal.\n",
+    "Under symmetric noise $\\mathbb{E}[\\max(z,0)]$ is only a fraction of a standard\n",
+    "deviation, so with $\\kappa$ above that value $S$ climbs at a roughly constant\n",
+    "rate and crosses any fixed threshold after a roughly fixed number of steps. The\n",
+    "rule therefore cannot tell \"converged\" from \"still improving, but too slowly\n",
+    "for this horizon to see\"; it announces the same thing at the same pace in\n",
+    "both cases. So the fix is not a better threshold but a wider horizon, plus a\n",
+    "second yardstick that asks whether the remaining improvement is negligible\n",
+    "relative to the reduction already achieved. That design is implemented in\n",
+    "[pymc-extras#733](https://github.com/pymc-devs/pymc-extras/pull/733); this\n",
+    "notebook shows the observation problem it exists to solve rather than shipping\n",
+    "a second copy of it.\n",
+    ":::\n",
+    "\n",
+    "How much would an online rule be worth here? The retrospective answer is a\n",
+    "benchmark you can only compute afterwards, which is why the online version is\n",
+    "needed:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "id": "6899295a",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:13:23.005333Z",
+     "iopub.status.busy": "2026-08-18T19:13:23.005204Z",
+     "iopub.status.idle": "2026-08-18T19:13:23.008151Z",
+     "shell.execute_reply": "2026-08-18T19:13:23.007755Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "stage 2 smoothed reduction: 188.11 nats;\n",
+      "99% of it reached by step 1,947 of 2,400 (81% of the stage-2 budget)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# t99: the first step at which a trailing average of width one epoch has covered\n",
+    "# 99% of stage 2's total smoothed reduction. Retrospective by construction.\n",
+    "kernel = np.ones(steps_per_epoch) / steps_per_epoch\n",
+    "smoothed = np.convolve(stage2, kernel, mode=\"valid\")\n",
+    "total_drop = smoothed[0] - smoothed.min()\n",
+    "# \"valid\" convolution starts at the first full window, so index j of `smoothed`\n",
+    "# is the trailing average ending at stage-2 step j + steps_per_epoch, counting\n",
+    "# steps from 1.\n",
+    "t99 = int(np.argmax(smoothed <= smoothed.min() + 0.01 * total_drop)) + steps_per_epoch\n",
+    "print(\n",
+    "    f\"stage 2 smoothed reduction: {total_drop:,.2f} nats;\\n\"\n",
+    "    f\"99% of it reached by step {t99:,} of {len(stage2):,} \"\n",
+    "    f\"({t99 / len(stage2):.0%} of the stage-2 budget)\"\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9d1d4cb4",
+   "metadata": {},
+   "source": [
+    "Any stopping rule, this one included, comes with two qualifications. What such a rule\n",
+    "detects is a *loss plateau*, a necessary signal, not a proof that the\n",
+    "posterior has converged; the recovery and predictive checks above are the kind\n",
+    "of independent evidence a stop should be paired with. And a plateau in a noisy\n",
+    "loss can only ever be established relative to a horizon: at any finite step, an\n",
+    "improvement small enough is indistinguishable from none.\n",
+    "\n",
+    "## Related tooling in pymc-extras\n",
+    "\n",
+    "The loader used above is one of four streaming pieces in pymc-extras. The\n",
+    "others are not imported here; this is where each one fits.\n",
+    "\n",
+    "| Component | What it does |\n",
+    "| --- | --- |\n",
+    "| [`DataLoader` / `parquet_source`](https://github.com/pymc-devs/pymc-extras/pull/698) | Turns an out-of-core source into minibatches and owns `total_size`; used directly above |\n",
+    "| [`Trainer`](https://github.com/pymc-devs/pymc-extras/pull/710) | Wraps the data-advance lifecycle around `pm.fit`, the job the twenty-line `StreamAdvance` does here |\n",
+    "| [`CheckLossConvergence`](https://github.com/pymc-devs/pymc-extras/pull/733) | Loss-based stopping on growing block horizons with two yardsticks; the observation problem it addresses is the previous section |\n",
+    "| [streaming Pathfinder](https://github.com/pymc-devs/pymc-extras/pull/722) | A short quasi-Newton run on minibatch gradients that returns a Gaussian proposal, importance-corrected against the full-data log-density, with Pareto-$k$ as its own veto |\n",
+    "\n",
+    "Pathfinder is the faster route when an approximately placed starting point is\n",
+    "what you need, for example to initialize Markov chain Monte Carlo (MCMC). It is not run here, and the\n",
+    "reason is a decision taken before fitting rather than a result: its documented\n",
+    "operating range is non-hierarchical targets of at most a few tens of\n",
+    "parameters, and this target is hierarchical with"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "fd8056a3",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:13:23.009404Z",
+     "iopub.status.busy": "2026-08-18T19:13:23.009302Z",
+     "iopub.status.idle": "2026-08-18T19:13:23.036685Z",
+     "shell.execute_reply": "2026-08-18T19:13:23.036298Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "free parameters in the unconstrained space: 154\n"
+     ]
+    }
+   ],
+   "source": [
+    "n_free = DictToArrayBijection.map(model.initial_point()).data.size\n",
+    "print(f\"free parameters in the unconstrained space: {n_free}\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "9c9df534",
+   "metadata": {},
+   "source": [
+    "free parameters, an order of magnitude outside that range. The ADVI results\n",
+    "above stand or fall on their own recovery and predictive checks, independently\n",
+    "of this choice."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "1c2eb643",
+   "metadata": {},
+   "source": [
+    "## Acknowledgements\n",
+    "\n",
+    "This notebook was written as part of the 2026\n",
+    "[Google Summer of Code](https://summerofcode.withgoogle.com/) project\n",
+    "*Streaming Variational Inference for Large Datasets* with PyMC and\n",
+    "[NumFOCUS](https://numfocus.org/), mentored by Rob Zinkov and Chris Fonnesbeck."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b74846ff",
+   "metadata": {},
+   "source": [
+    "## Authors\n",
+    "\n",
+    "* Authored by [Yicheng Yang](https://github.com/YichengYang-Ethan) in August\n",
+    "  2026 ([pymc-examples#892](https://github.com/pymc-devs/pymc-examples/pull/892))"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "abd92be7",
+   "metadata": {},
+   "source": [
+    "## References\n",
+    "\n",
+    ":::{bibliography}\n",
+    ":filter: docname in docnames\n",
+    ":::"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "a133b6c3",
+   "metadata": {},
+   "source": [
+    "## Watermark"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "id": "a63ff46e",
+   "metadata": {
+    "execution": {
+     "iopub.execute_input": "2026-08-18T19:13:23.038391Z",
+     "iopub.status.busy": "2026-08-18T19:13:23.038275Z",
+     "iopub.status.idle": "2026-08-18T19:13:23.076341Z",
+     "shell.execute_reply": "2026-08-18T19:13:23.075745Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "wall time for every cell above, on this machine: 42 s\n",
+      "Last updated: Tue, 18 Aug 2026\n",
+      "\n",
+      "Python implementation: CPython\n",
+      "Python version       : 3.12.12\n",
+      "IPython version      : 9.16.1\n",
+      "\n",
+      "xarray: 2026.7.0\n",
+      "\n",
+      "arviz      : 1.3.0\n",
+      "logging    : 0.5.1.2\n",
+      "matplotlib : 3.11.1\n",
+      "numpy      : 2.4.6\n",
+      "pandas     : 3.0.5\n",
+      "pyarrow    : 25.0.1\n",
+      "pymc       : 6.2.0\n",
+      "pymc_extras: 0.14.1.dev3+g8db1880d4\n",
+      "pytensor   : 3.2.4\n",
+      "scipy      : 1.18.0\n",
+      "\n",
+      "Watermark: 2.6.0\n",
+      "\n",
+      "Compiler    : Clang 21.1.4 \n",
+      "OS          : Darwin\n",
+      "Release     : 24.6.0\n",
+      "Machine     : arm64\n",
+      "Processor   : arm\n",
+      "CPU cores   : 10\n",
+      "Architecture: 64bit\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "%load_ext watermark\n",
+    "print(f\"wall time for every cell above, on this machine: {time.perf_counter() - NOTEBOOK_T0:.0f} s\")\n",
+    "%watermark -n -u -v -iv -w -p xarray\n",
+    "%watermark -m"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "beb2c98c",
+   "metadata": {},
+   "source": [
+    ":::{include} ../page_footer.md\n",
+    ":::"
+   ]
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "default_lexer": "ipython3"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.12"
+  },
+  "myst": {
+   "substitutions": {
+    "extra_dependencies": "pyarrow"
+   }
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/examples/variational_inference/streaming_tick_data.myst.md b/examples/variational_inference/streaming_tick_data.myst.md
new file mode 100644
index 000000000..c2feab0c1
--- /dev/null
+++ b/examples/variational_inference/streaming_tick_data.myst.md
@@ -0,0 +1,1458 @@
+---
+jupytext:
+  default_lexer: ipython3
+  text_representation:
+    extension: .md
+    format_name: myst
+    format_version: 0.13
+kernelspec:
+  display_name: Python 3 (ipykernel)
+  language: python
+  name: python3
+myst:
+  substitutions:
+    extra_dependencies: pyarrow
+---
+
+(streaming_tick_data)=
+
+# Streaming variational inference on high-frequency tick data
+
+:::{post} August 17, 2026
+:tags: variational inference, minibatch, out-of-core, hierarchical model, time series
+:category: advanced, tutorial
+:author: Yicheng Yang
+:::
+
++++
+
+Exchanges publish per-trade archives; Binance, for instance, distributes
+aggregate trades as daily and monthly files at
+[data.binance.vision](https://data.binance.vision). Widen a pull across
+symbols and months and the feature matrix can outgrow the RAM of an ordinary
+workstation; at that point in-memory minibatching stops being an option. PyMC's
+{func}`~pymc.Minibatch` randomly slices tensor *inputs*; it is not itself a
+disk-backed reader, so it cannot help once the array no longer fits. This
+notebook fits a hierarchical hurdle–Student-t model of *next-event price
+moves* by streaming minibatches from disk with pymc-extras'
+[`DataLoader`](https://github.com/pymc-devs/pymc-extras/blob/8db1880d410e509be02abf9b085f08c3d4514fd1/pymc_extras/variational/dataloader.py),
+on 300,000 synthetic
+rows that are generated inside the notebook. It tries to teach three things:
+
+1. **When minibatch variational inference (VI) is even valid.** Two acceptance gates that most
+   time-series models fail, and a model class that passes both.
+2. **The mechanics, end to end** on an archive small enough to rebuild here:
+   an on-disk global shuffle, streaming automatic differentiation variational
+   inference (ADVI) with `total_size` rescaling, and
+   what a stopping rule has to be able to see before it can fire.
+3. **Checking the recovery.** With the truth known, recovery is checkable once
+   one correction is made: the cyclic replay puts the optimizer on an orbit, and
+   the last iterate misses the truth by several of its own posterior standard
+   deviations purely because of where in that orbit the step budget ended.
+   Averaged over one full pass, every identified row the recovery table reports
+   lands inside 1.2 posterior standard deviations of its generating value,
+   across three fits, one of them on a different replay order. Whether the
+   widths themselves are calibrated is a separate question one dataset cannot
+   settle; no claim about them is made here.
+
+The notebook times itself; the watermark at the end reports the wall time and the machine. Everything below executes on synthetic
+data with known ground truth, which is what makes the recovery checks
+possible: nothing here is a claim about any real market.
+
++++
+
+## Two gates: when minibatch VI is valid, and when it is worth it
+
+Minibatch variational inference rests on one identity: if the likelihood
+factors over rows given the parameters, then the batch log-likelihood scaled by
+$N/b$ is an unbiased estimator of the full-data log-likelihood
+{cite:p}`hoffman2013stochastic`. Two gates shaped the model in this notebook. The first is about *validity*;
+the second is about whether streaming is doing any real work. Check both
+before reaching for `total_size` on your own data:
+
+**Gate 1 — validity: the likelihood must factor over rows, and the batching
+must weight every row equally.** No latent path coupling observations, no
+label shared across rows, and every row given the same inclusion frequency by
+the batching scheme (unequal inclusion breaks the plain $N/b$ rescaling). How
+the batches are *drawn* is a separate matter that this gate does not settle:
+fresh uniform batches make each step's scaled gradient conditionally unbiased
+for the full-data objective (the identity above), while a fixed shuffled
+order replayed every epoch visits each row once per pass, targets the same
+finite sum, and yields cyclic rather than unbiased updates. This notebook does
+the second, and returns to the distinction where the fit is described. The
+factorization requirement is exactly why the classic
+stochastic volatility model of the {ref}`stochastic_volatility` notebook
+*cannot* be minibatched: its latent volatility path ties every observation to
+its neighbors, so a random subset of rows does not carry $b/N$ of the
+log-likelihood. Any model whose rows share one outcome (for example, every tick
+of a match sharing the final result) fails the same gate through
+pseudo-replication: the effective sample size is the number of outcomes, not
+the number of rows.
+
+**Gate 2 — non-triviality: no low-dimensional sufficient statistics.** This
+one is about whether the streaming machinery is needed at all, not about
+validity: a Normal
+likelihood with per-cell means and variances collapses to per-cell
+$(\sum y, \sum y^2, n)$, so one linear scan computes the exact posterior
+inputs and "streaming inference" degenerates into a glorified `groupby`:
+valid, but theater. A hurdle alone does not rescue it; an observed Bernoulli
+plus a Normal component still reduces to $(n_0, n_1, \sum y, \sum y^2)$. What
+does break the collapse is the combination used below: a Student-t component
+with an unknown $\nu$, and row-level continuous covariates entering through
+nonlinear links, so no fixed-dimensional summary of the rows suffices.
+
+The model below passes both gates. The case for each ingredient is that it is
+the standard choice for the data feature it handles, not that it fits the
+synthetic data, which was written to contain those features in the first
+place.
+
++++
+
+:::{include} ../extra_installs.md
+:::
+
+:::{note}
+The `DataLoader` merged into
+pymc-extras after the v0.14.0 release, so it is not in a published version yet
+and `pip install pymc-extras` will not provide it. The outputs stored in this
+notebook were produced against the merge commit itself, which is also the
+install line to use until the next release:
+
+```
+pip install git+https://github.com/pymc-devs/pymc-extras@8db1880
+```
+
+That pins pymc-extras `0.14.1.dev3+g8db1880d4`, and its own requirements pull
+PyMC 6.2 and PyTensor 3.2, the versions reported by the watermark at the
+bottom of this page. Once the module ships, `pip install pymc-extras` will do.
+:::
+
+```{code-cell} ipython3
+import gc
+import logging
+import os
+import tempfile
+import time
+import warnings
+
+import arviz as az
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import pyarrow as pa
+import pyarrow.parquet as pq
+import pymc as pm
+import pytensor.tensor as pt
+
+from matplotlib.ticker import StrMethodFormatter
+from pymc.blocking import DictToArrayBijection
+from pymc_extras.variational.dataloader import DataLoader, parquet_source
+from scipy import stats
+
+NOTEBOOK_T0 = time.perf_counter()
+```
+
+```{code-cell} ipython3
+%config InlineBackend.figure_format = 'retina'
+RANDOM_SEED = 20260731
+rng = np.random.default_rng(RANDOM_SEED)
+az.style.use("arviz-variat")
+# the loss figures carry the fit story; keep the fit logger's lines out of the output
+logging.getLogger("pymc").setLevel(logging.ERROR)
+# the predictive path compiles the custom `random` and the minibatch wrapper through
+# numba, which falls back to object mode for both and says so; not actionable here
+warnings.filterwarnings("ignore", message=r"Numba will use object mode")
+```
+
+## The model: hierarchical hurdle–Student-t next-event returns
+
+One row is one trade-to-trade transition $i$ on symbol $s$ at UTC hour $h$:
+the return $y_i = 10^4 \, (\log p_{i+1} - \log p_i)$ in basis points, and the
+move indicator $m_i = \mathbb{1}[y_i \neq 0]$. Indexing by *events* rather than
+by clock time has two consequences that matter before the algebra.
+
+First, prices move on a discrete grid, so an exact-zero return is not a
+measure-zero event the way it is for a continuous variable: consecutive trades
+can and do leave the price where it was. A purely continuous likelihood puts
+zero probability mass on that outcome, whatever share of the rows it turns out
+to occupy. The fix is a hurdle: model *whether* the price moves separately
+from *how far* it moves given that it does.
+
+Second, each row is a transition between consecutive observed events, not a
+fixed-duration return. So $1 - \pi$ is the probability that the next event
+leaves the price unchanged, and $\sigma$ is the Student-t scale *conditional on
+a move*. Neither is clock-time volatility: converting to a clock would
+additionally require a model of event arrival times
+{cite:p}`engle1998autoregressive`.
+
+$$
+\begin{aligned}
+m_i &\sim \text{Bernoulli}(\pi_i) \\
+y_i \mid m_i = 1 &\sim \text{StudentT}(\nu,\ \mu_i,\ \sigma_i) \\
+\operatorname{logit} \pi_i &= \kappa_0 + b^{(\kappa)}_s
+  + B(h)^\top\!\left(c + b^{(\pi h)}_s\right)
+  + \lambda_a a_i + \lambda_q q_i \\
+\log \sigma_i &= \alpha_0 + b^{(\alpha)}_s
+  + B(h)^\top\!\left(g + b^{(\sigma h)}_s\right)
+  + (\beta_a + b^{(\beta a)}_s)\, a_i + \beta_q q_i \\
+\mu_i &= \theta_d\, d_i + \theta_r\, \text{ylag}_i
+\end{aligned}
+$$
+
+where
+
+* $\kappa_0$ and $\alpha_0$ are the global intercepts of the move probability
+  and the log scale, and $b^{(\kappa)}_s$, $b^{(\alpha)}_s$ their per-symbol
+  offsets;
+* $B(h)$ is the hour-of-day Fourier basis (four columns), $c$ and $g$ its
+  global coefficients, and $b^{(\pi h)}_s$, $b^{(\sigma h)}_s$ the per-symbol
+  deviations from them;
+* $\lambda_a$, $\lambda_q$, $\beta_a$, $\beta_q$ are the covariate slopes, with
+  $b^{(\beta a)}_s$ a per-symbol slope on activity in the scale;
+* $\theta_d$, $\theta_r$ place the conditional location on the trade sign and
+  the last nonzero return;
+* every $b_s \sim \mathcal{N}(0, \tau)$ with its own $\tau$, and $\nu$ is shared.
+
+The covariates are a simulated trade sign $d_i$, a notional-like $q_i$, an
+activity-like $a_i$, and $\text{ylag}_i$, the most recent previous nonzero
+return on that symbol. In the generator below they are drawn directly rather
+than engineered from a trade tape: only $\text{ylag}$ is produced
+sequentially, so it is the one covariate that could not see the future, and
+$q$ and $a$ are standardized once over the whole sample. On a real archive
+both would instead be trailing statistics built causally from the tape and
+standardized with constants frozen on a burn-in window, a construction that
+is out of scope here and easy to get subtly wrong. $B(h)$ is the first two sine/cosine harmonics of hour-of-day,
+a low-order version of the Fourier encoding of intraday periodicity in
+{cite:t}`andersen1997intraday`; heavy-tailed Student-t noise for returns goes
+back at least to {cite:t}`bollerslev1987conditionally`. All symbol effects
+$b_s \sim \mathcal{N}(0, \tau)$ are partially pooled {cite:p}`gelman2006data`,
+so a thin symbol is pulled toward the global intraday shape where its own data
+run out. They are parameterized
+*centered*, deliberately: the non-centered trick pays off when groups are
+data-poor, and the generator below gives every symbol at least several hundred
+rows.
+The degrees of freedom are
+shared across symbols, parameterized $\nu = 1 + \operatorname{softplus}(\eta)$;
+the floor of 1 rather than 2 leaves the support open to tails too heavy to
+carry a finite variance. Under $\eta \sim \mathcal{N}(5, 1)$ the prior puts
+only about $4 \times 10^{-6}$ of
+its mass on $\nu \le 2$, so this is a statement about support, not a serious
+prior belief in infinite variance. The estimand below is chosen so that the
+question does not arise at all.
+
+The reported estimand is **event-return dispersion on the event clock**:
+$\pi_{s,h}$ together with the conditional-move 90% half-width
+$\sigma_{s,h} \cdot t^{-1}_{0.95}(\nu)$. A quantile is the safe choice here: it
+stays finite for any $\nu > 0$, whereas a moment-based dispersion requires
+$\nu > 2$, a constraint the model does not impose, even though the generator
+below happens to use $\nu = 3.5$.
+
++++
+
+## Synthetic data with known truth
+
+This notebook neither ships nor downloads an exchange archive; the executed
+path uses a seeded synthetic generator: the schema an exchange
+archive would have after feature construction, a hurdle at exactly zero,
+heavy conditional tails, and twelve symbols whose row counts span two orders
+of magnitude, so the hierarchy has both data-rich and data-poor groups to work
+with. Because the truth is known, recovery is checkable. Every
+statement below about what the fit recovers is conditional on this generator;
+none of it is a measurement of any market.
+
+```{code-cell} ipython3
+n_symbols = 12
+counts = np.array(
+    [90_700, 55_000, 40_000, 30_000, 24_000, 19_000, 15_000, 11_000, 8_000, 4_600, 1_800, 900]
+)
+thin = [10, 11]  # the two symbols with the least data
+
+truth = {
+    "kappa0": np.log(0.7 / 0.3),  # logit(0.7): see the annotation below
+    "c": np.array([0.25, -0.15, 0.10, 0.05]),
+    "lambda_a": 0.35,
+    "lambda_q": 0.20,
+    "alpha0": np.log(0.05),  # conditional-move scale, in basis points
+    "g": np.array([0.20, -0.12, 0.08, 0.04]),
+    "beta_a": 0.18,
+    "beta_q": 0.12,
+    "theta_d": 0.02,
+    "theta_r": 0.25,
+    "nu": 3.5,
+}
+
+
+def standardize(x, axis=0):
+    return (x - x.mean(axis=axis, keepdims=True)) / x.std(axis=axis, keepdims=True)
+
+
+z_truth = {
+    "z_k": standardize(rng.standard_normal(n_symbols)),
+    "z_ph": standardize(rng.standard_normal((n_symbols, 4))),
+    "z_al": standardize(rng.standard_normal(n_symbols)),
+    "z_sh": standardize(rng.standard_normal((n_symbols, 4))),
+    "z_ba": standardize(rng.standard_normal(n_symbols)),
+}
+```
+
+```{code-cell} ipython3
+def hour_basis(hour):
+    w = 2 * np.pi * np.asarray(hour, dtype=float) / 24.0
+    return np.column_stack([np.sin(w), np.cos(w), np.sin(2 * w), np.cos(2 * w)])
+
+
+sym = np.repeat(np.arange(n_symbols), counts)
+n = len(sym)
+hour = rng.integers(0, 24, size=n)
+d = rng.choice([-1.0, 1.0], size=n)
+a = standardize(rng.standard_normal(n))  # trailing activity (already standardized)
+q = standardize(0.3 * a + np.sqrt(1 - 0.3**2) * rng.standard_normal(n))
+
+B = hour_basis(hour)
+logit_pi = (
+    truth["kappa0"]
+    + 0.30 * z_truth["z_k"][sym]
+    + B @ truth["c"]
+    + (B * (0.15 * z_truth["z_ph"][sym])).sum(1)
+    + truth["lambda_a"] * a
+    + truth["lambda_q"] * q
+)
+log_sigma = (
+    truth["alpha0"]
+    + 0.35 * z_truth["z_al"][sym]
+    + B @ truth["g"]
+    + (B * (0.12 * z_truth["z_sh"][sym])).sum(1)
+    + (truth["beta_a"] + 0.10 * z_truth["z_ba"][sym]) * a
+    + truth["beta_q"] * q
+)
+
+m = (rng.random(n) < 1 / (1 + np.exp(-logit_pi))).astype(np.int8)
+t_draw = rng.standard_t(truth["nu"], size=n)
+
+# sequential generation per symbol so ylag feeds back causally
+y = np.zeros(n)
+ylag = np.zeros(n)
+sigma = np.exp(log_sigma)
+for s in range(n_symbols):
+    idx = np.flatnonzero(sym == s)
+    last = 0.0
+    for i in idx:
+        ylag[i] = last
+        if m[i]:
+            y[i] = truth["theta_d"] * d[i] + truth["theta_r"] * last + sigma[i] * t_draw[i]
+            last = y[i]
+
+print(
+    f"{n:,} rows, zero-move share {(m == 0).mean():.1%}, "
+    f"median nonzero |y| {np.median(np.abs(y[m == 1])):.3f} bp, "
+    f"corr(a, q) {np.corrcoef(a, q)[0, 1]:.2f}"
+)
+```
+
+The constants above are settings, not measurements. Read them as the
+specification of the simulator:
+
+* `kappa0` = $\operatorname{logit}(0.7)$. If every other term in the logit were
+  zero, the move probability would be 0.7. The harmonics, the covariates and
+  the symbol effects all shift it, so the *realized* zero share is the number
+  printed above, not $30\%$ by construction.
+* `theta_r` $= 0.25$ imposes positive first-order dependence in the conditional
+  location: the next move is generated partly from the previous nonzero one. It
+  is a synthetic dependence parameter; note that the classical bid–ask bounce
+  of {cite:t}`roll1984simple` runs the other way, inducing *negative* serial
+  dependence, which this generator does not encode.
+* `theta_d` $= 0.02$ is the coefficient on the simulated trade sign: holding
+  everything else fixed, sign $+1$ versus $-1$ differs by 0.04 bp in
+  conditional location.
+* $a$ and $q$ are constructed with a target correlation of 0.3 rather than
+  orthogonally, so the fit faces mildly collinear covariates instead of a
+  textbook design; the realized sample correlation is in the printout above.
+* `ylag` is generated per symbol in sequence, so row $i$ sees only the most
+  recent previously generated nonzero move. That discipline applies to the lag
+  construction; the standardization of $a$ and $q$ still uses full-sample
+  constants, an offline simplification kept for readability.
+
+With that in mind, here is the feature that forces the hurdle:
+
+```{code-cell} ipython3
+fig, ax = plt.subplots(figsize=(8, 3.5), layout="constrained")
+moves = y[m == 1]
+ax.hist(moves, bins=201, range=(-1.5, 1.5), log=True, color="C0", label="nonzero moves")
+ax.bar(
+    [0.0],
+    [(m == 0).sum()],
+    width=0.02,
+    color="C1",
+    label=f"exactly zero ({(m == 0).mean():.0%} of rows)",
+)
+ax.set_xlabel("next-event return (bp)")
+ax.set_ylabel("count (log scale)")
+ax.set_title("A continuous density puts zero mass on the most common outcome")
+ax.legend();
+```
+
+## The on-disk global shuffle
+
+Streaming ordered data has one trap that is easy to miss. A bounded runtime shuffle buffer only *block*-shuffles a strongly
+ordered stream: with tick data sorted by symbol and time, early optimization
+steps would only ever see early dates and the first symbols, and an early
+stopping decision would be biased by construction. The fix is to shuffle
+**once, globally, on disk when the archive is written**: every row gets a deterministic hash
+key, rows are scattered across shards by that key, and each shard is sorted by
+it. After that, sequential reads follow one fixed, data-independent
+permutation. It is pseudo-random (a hash of the row index, nothing from the
+row itself) and replayed identically each epoch, so this is single-shuffle stochastic gradient descent
+rather than fresh per-step subsampling. The loader can then run with
+`shuffle=False`, without a copy through the shuffle buffer.
+
+```{code-cell} ipython3
+data_dir = tempfile.mkdtemp(prefix="ticks_")
+table = pa.table(
+    {
+        "y_bp": y.astype(np.float32),
+        "m": m,
+        "d": d.astype(np.int8),
+        "q_std": q.astype(np.float32),
+        "a_std": a.astype(np.float32),
+        "ylag_bp": ylag.astype(np.float32),
+        "hour": hour.astype(np.int8),
+        "sym": sym.astype(np.int16),
+    }
+)
+
+
+def splitmix64(x):
+    x = (x + np.uint64(0x9E3779B97F4A7C15)) & np.uint64(0xFFFFFFFFFFFFFFFF)
+    x = ((x ^ (x >> np.uint64(30))) * np.uint64(0xBF58476D1CE4E5B9)) & np.uint64(0xFFFFFFFFFFFFFFFF)
+    x = ((x ^ (x >> np.uint64(27))) * np.uint64(0x94D049BB133111EB)) & np.uint64(0xFFFFFFFFFFFFFFFF)
+    return x ^ (x >> np.uint64(31))
+
+
+key = splitmix64(np.arange(n, dtype=np.uint64))
+order = np.argsort(key, kind="stable")
+n_shards, BATCH = 10, 1_000
+# Row groups are the loader's batches on the shuffle=False path, so the geometry is
+# chosen to divide exactly: 10 shards x 30,000 rows, 30 groups of 1,000 each.
+assert n % n_shards == 0 and (n // n_shards) % BATCH == 0
+for i in range(n_shards):
+    part = table.take(order[i::n_shards])
+    pq.write_table(part, os.path.join(data_dir, f"shard_{i:03d}.parquet"), row_group_size=BATCH)
+
+# the head of every shard has to mix hours and symbols already; check all of them
+for i in range(n_shards):
+    head = pq.read_table(os.path.join(data_dir, f"shard_{i:03d}.parquet")).slice(0, 10_000)
+    assert len(np.unique(head["hour"])) == 24
+    assert len(np.unique(head["sym"])) == n_symbols
+print(f"first 10k rows of every shard cover all 24 hours and all {n_symbols} symbols")
+```
+
+The assertion is the one to keep: the head of every shard must already mix
+every hour and every symbol, checked rather than assumed.
+
+This cell is a small-scale stand-in for the real extract-transform-load step: it builds the whole
+table, the whole key array and the whole permutation in memory, which is
+exactly what one cannot do once the data stops fitting. The inference that
+follows really does read from disk; the preprocessing above does not. An out-of-core design with the same properties is two passes:
+assign each row to a shard from its hash key, appending row groups of bounded
+size, then sort each shard by key independently, so no step holds more than
+one shard. That yields a different permutation from the rank-based split used
+here (shard sizes come out approximately rather than exactly equal), with the
+same guarantee that no shard's order depends on anything in the rows.
+
+So that the fit really does run against disk rather than against arrays still
+resident from the generator, the row-scale objects are released first:
+
+```{code-cell} ipython3
+del table, part, head, key, order, y, m, d, q, a, ylag, sym, hour, B, moves
+del logit_pi, log_sigma, t_draw, sigma
+gc.collect()
+print("row-scale generator arrays released; the fit reads from", os.path.basename(data_dir))
+```
+
+## Streaming the model
+
+With `shuffle=False` the `DataLoader` passes source blocks through verbatim,
+one block per Parquet row group, in a frozen column order. That is why the
+shards above were written with `row_group_size` equal to the batch size, and
+why the row counts were chosen to divide exactly. On this path the loader
+hands you the row groups it finds. A ragged geometry therefore gives ragged
+batches; `len(loader)` (which is `total_size // batch_size`) stops matching the
+number of blocks an epoch yields; and the recorded loss picks up a
+deterministic sawtooth that has nothing to do with convergence (the fitting
+section explains why the recorded value scales with the block size). The model reads
+one `pm.Data` placeholder; everything derived (the Fourier basis, the
+integer symbol index) is computed inside the graph, so advancing the stream
+is a single `set_value` per step.
+
+```{code-cell} ipython3
+columns = ["y_bp", "m", "d", "q_std", "a_std", "ylag_bp", "hour", "sym"]
+loader = DataLoader(
+    parquet_source(data_dir, columns=columns),
+    batch_size=BATCH,
+    shuffle=False,  # the shards are already globally shuffled on disk
+    total_size="auto",
+)
+
+# Count one epoch rather than inferring it. With a divisible geometry every block
+# is the same size, so the count and len(loader) agree — which is what the rest of
+# the notebook relies on.
+block_rows = [b.shape[0] for b in loader]
+steps_per_epoch = len(block_rows)
+assert set(block_rows) == {BATCH}
+assert sum(block_rows) == loader.total_size == n == steps_per_epoch * BATCH
+assert len(loader) == steps_per_epoch
+print(
+    f"N = {loader.total_size:,} rows -> {steps_per_epoch} blocks of {BATCH} per epoch,\n"
+    f"conserving {sum(block_rows):,} rows; len(loader) = {len(loader)}"
+)
+```
+
+That assertion pins down two things. First, **nothing is dropped**: verbatim
+pass-through streams every row exactly once per epoch. (Ragged geometry would
+still lose nothing, since PyMC reads `b` from the batch actually installed and
+a short block is weighted up by its own size, but it would cost the clean
+diagnostics below, which is why the shards divide.) Only the shuffle-buffer
+path drops a trailing partial batch, and this notebook never uses it.
+
+Second, this is where the Gate 1 distinction bites: the batch at step $t$ is a
+deterministic function of $t$, so what follows is single-shuffle, cyclic
+finite-sum optimization, the arrangement the epoch-scale diagnostics later in
+the notebook exploit and the reason the fit has to be summarized with some
+care before anything is read off it.
+
+```{code-cell} ipython3
+def build_model(symbols, batch_init, total_size):
+    coords = {"symbol": list(symbols), "harmonic": ["sin1", "cos1", "sin2", "cos2"]}
+    with pm.Model(coords=coords) as model:
+        batch = pm.Data("batch", batch_init)
+        y_ = batch[:, 0]
+        d_ = batch[:, 2]
+        q_ = batch[:, 3]
+        a_ = batch[:, 4]
+        ylag_ = batch[:, 5]
+        w = 2.0 * np.pi * batch[:, 6] / 24.0
+        B_ = pt.stack([pt.sin(w), pt.cos(w), pt.sin(2 * w), pt.cos(2 * w)], axis=1)
+        sym_ = pt.cast(batch[:, 7], "int32")
+
+        kappa0 = pm.Normal("kappa0", 0.0, 5.0)
+        c = pm.Normal("c", 0.0, 0.5, dims="harmonic")
+        lambda_a = pm.Normal("lambda_a", 0.0, 0.5)
+        lambda_q = pm.Normal("lambda_q", 0.0, 0.5)
+
+        alpha0 = pm.Normal("alpha0", 0.0, 5.0)
+        g = pm.Normal("g", 0.0, 0.3, dims="harmonic")
+        beta_a = pm.Normal("beta_a", 0.0, 0.3)
+        beta_q = pm.Normal("beta_q", 0.0, 0.3)
+
+        theta_d = pm.Normal("theta_d", 0.0, 0.25)
+        theta_r = pm.Normal("theta_r", 0.0, 0.25)
+
+        tau_k, tau_ph, tau_al, tau_sh, tau_ba = (
+            pm.LogNormal(name, 0.0, 1.5)
+            for name in ["tau_k", "tau_ph", "tau_al", "tau_sh", "tau_ba"]
+        )
+        b_k = pm.Normal("b_k", 0.0, tau_k, dims="symbol")
+        b_ph = pm.Normal("b_ph", 0.0, tau_ph, dims=("symbol", "harmonic"))
+        b_al = pm.Normal("b_al", 0.0, tau_al, dims="symbol")
+        b_sh = pm.Normal("b_sh", 0.0, tau_sh, dims=("symbol", "harmonic"))
+        b_ba = pm.Normal("b_ba", 0.0, tau_ba, dims="symbol")
+
+        eta = pm.Normal("eta", 5.0, 1.0)
+        nu = pm.Deterministic("nu", 1.0 + pt.softplus(eta))
+
+        logit_pi = (
+            kappa0
+            + b_k[sym_]
+            + (B_ * (c + b_ph[sym_])).sum(axis=-1)
+            + lambda_a * a_
+            + lambda_q * q_
+        )
+        log_sigma = (
+            alpha0
+            + b_al[sym_]
+            + (B_ * (g + b_sh[sym_])).sum(axis=-1)
+            + (beta_a + b_ba[sym_]) * a_
+            + beta_q * q_
+        )
+        mu = theta_d * d_ + theta_r * ylag_
+
+        def hurdle_logp(value, logit_pi, mu, log_sigma, nu):
+            # one coherent mixed distribution: an atom at exactly zero plus a
+            # Student-t density off zero — the move indicator is value != 0,
+            # never a separate parameter, so logp and random describe the SAME law
+            sigma = pt.exp(log_sigma)
+            t_ll = (
+                pt.gammaln((nu + 1.0) / 2.0)
+                - pt.gammaln(nu / 2.0)
+                - 0.5 * pt.log(nu * np.pi)
+                - log_sigma
+                - (nu + 1.0) / 2.0 * pt.log1p(((value - mu) / sigma) ** 2 / nu)
+            )
+            moved = pt.neq(value, 0.0)
+            return pt.where(moved, -pt.softplus(-logit_pi) + t_ll, -pt.softplus(logit_pi))
+
+        def hurdle_random(logit_pi, mu, log_sigma, nu, rng=None, size=None):
+            # simulate the same law: first whether the price moves, then how far
+            pi = 1.0 / (1.0 + np.exp(-logit_pi))
+            move = rng.random(size=size) < pi
+            draw = mu + np.exp(log_sigma) * rng.standard_t(nu, size=size)
+            return np.where(move, draw, 0.0)
+
+        pm.CustomDist(
+            "y_obs",
+            logit_pi,
+            mu,
+            log_sigma,
+            nu,
+            logp=hurdle_logp,
+            random=hurdle_random,
+            observed=y_,
+            total_size=total_size,
+        )
+    return model
+```
+
+Two implementation notes. The likelihood is a
+{class}`~pymc.CustomDist` with a `logp`, not a `pm.Potential`: `CustomDist`
+gives the term *observed random-variable semantics*, which is what makes PyMC's own
+`total_size` minibatch rescaling apply. And
+the hurdle's two logs are written with `softplus`,
+$\log \pi = -\operatorname{softplus}(-x)$ and
+$\log(1-\pi) = -\operatorname{softplus}(x)$, which is exact and stable at both
+tails. The exact float comparison `value != 0` is safe here because the zeros are
+*structural*: the generator writes an exact 0.0 when the hurdle says the price
+did not move, and every nonzero draw is many orders of magnitude above the
+float32 subnormal range. On real data the same comparison is safe as long as
+returns are computed so that "no move" produces an exact zero rather than a
+rounding artifact; check that once, in the feature code.
+
+```{code-cell} ipython3
+model = build_model(
+    [f"SYM{i:02d}" for i in range(n_symbols)], next(iter(loader)), loader.total_size
+)
+pm.model_to_graphviz(model)
+```
+
+The plate diagram is an inventory rather than a picture of the two links: five
+partially pooled effect families on the symbol plate, one shared tail
+parameter, and a single observed node whose batch dimension is whatever the
+placeholder currently holds. The two linear predictors live inside the
+likelihood's `logp` and are not separate nodes; the equations above are where
+that structure is visible.
+
+The loop below is the whole streaming adapter: a `pm.fit` callback that pushes
+the next block into the placeholder after each step; the twenty lines here are
+the minimal version a notebook can own (a library wrapper for the same
+lifecycle is listed at the end).
+
+```{code-cell} ipython3
+class StreamAdvance:
+    """pm.fit callback: put the next minibatch into the placeholder after each step."""
+
+    def __init__(self, model, loader):
+        self._shared = model["batch"]
+        self._stream = self._endless(loader)
+
+    @staticmethod
+    def _endless(loader):
+        while True:
+            yield from loader
+
+    def prime(self):
+        self._shared.set_value(next(self._stream), borrow=True)
+
+    def __call__(self, approx, losses, i):
+        self._shared.set_value(next(self._stream), borrow=True)
+
+
+class ParamTrace:
+    """pm.fit callback: record the variational parameters after every step."""
+
+    def __init__(self):
+        self.mu, self.rho = [], []
+
+    def __call__(self, approx, losses, i):
+        mu, rho = approx.params
+        self.mu.append(mu.get_value())
+        self.rho.append(rho.get_value())
+
+
+stream = StreamAdvance(model, loader)
+stream.prime()
+tail = ParamTrace()
+
+with model:
+    advi = pm.ADVI(random_seed=RANDOM_SEED)
+advi.fit(6_000, obj_optimizer=pm.adam(learning_rate=0.02), callbacks=[stream], progressbar=False)
+approx = advi.fit(
+    2_400,
+    obj_optimizer=pm.adam(learning_rate=0.005),
+    callbacks=[stream, tail],
+    progressbar=False,
+)
+```
+
+The fit is mean-field ADVI {cite:p}`kucukelbir2015automatic` driven by Adam.
+The learning rate is cut once, at step 6,000. A constant step size that is
+comfortable early is too large near the optimum, where it keeps the
+variational mean bouncing instead of settling; dropping it once the descent
+has flattened is the cheapest fix. The effect does not show up in the width of
+the loss band below: the band is dominated by batch-composition noise (the
+stopping section shows it all but vanishing at epoch-aligned horizons), and
+the printed spread is the same on both sides of the change. It shows up in the
+level, as the printed one-epoch means either side of the cut, and in the
+parameter trace examined after the fit.
+
+Before plotting it, one property of `approx.hist` that is easy to get wrong and
+that changes what the numbers mean. PyMC normalizes the minibatch objective
+**twice**: the observed log-probability is scaled up by $N/b$ so the gradient
+targets the full-data model, and then the variational objective is divided by
+that same constant, because `scale_cost_to_minibatch` is on by default. What
+gets recorded per step is therefore
+
+$$
+F_t = -\sum_{i \in \mathcal{B}_t} \mathbb{E}_q\left[\log p(y_i \mid \theta)\right]
+      + \frac{b}{N}\,\mathrm{KL}(q \Vert p),
+$$
+
+where $\mathcal{B}_t$ is the set of rows in the batch at step $t$, $b$ its
+size, $N$ the total row count (`loader.total_size`), $\theta$ the parameters,
+$q$ the variational approximation, $p$ the prior, and $\mathrm{KL}$ the
+Kullback–Leibler divergence. This lives on the scale of *one batch*, not of
+the full dataset, and would move mechanically with $b$ if the blocks were
+ragged. Multiplying by
+$N/b$ puts it back on the full-data scale of the negative evidence lower bound
+(ELBO), and with equal blocks
+that is one constant:
+
+```{code-cell} ipython3
+ELBO_SCALE = loader.total_size / BATCH  # undo PyMC's scale_cost_to_minibatch
+loss = np.asarray(approx.hist, dtype=float) * ELBO_SCALE
+
+epoch_mean = np.convolve(loss, np.ones(steps_per_epoch) / steps_per_epoch, mode="valid")
+
+fig, ax = plt.subplots(figsize=(8, 3.2), layout="constrained")
+ax.plot(loss, lw=0.5, alpha=0.6, label="per step")
+ax.plot(
+    np.arange(steps_per_epoch - 1, len(loss)),
+    epoch_mean,
+    color="C1",
+    lw=1.5,
+    label="one-epoch moving mean",
+)
+ax.axvline(6_000, color="k", ls="--", lw=1, label="learning rate cut, 0.02 to 0.005")
+# clip to the plateau: the first few hundred steps are orders of magnitude higher
+plateau = loss[1_000:]
+ax.set_ylim(np.quantile(plateau, 0.001), np.quantile(plateau, 0.999))
+ax.yaxis.set_major_formatter(StrMethodFormatter("{x:,.0f}"))
+ax.set_xlabel("step")
+ax.set_ylabel("negative ELBO, full-data scale")
+ax.set_title("Streaming ADVI loss on the plateau (steps before 1,000 are off scale)")
+ax.legend(loc="upper right", fontsize=9, frameon=True)
+before, after = loss[6_000 - 2 * steps_per_epoch : 6_000], loss[6_000 : 6_000 + 2 * steps_per_epoch]
+print(
+    "recorded loss over the two passes either side of the cut:\n"
+    f"  spread (sd): {before.std():,.0f} before, {after.std():,.0f} after\n"
+    f"  level (mean): {before.mean():,.0f} before, {after.mean():,.0f} after"
+);
+```
+
+## The last iterate is not the answer
+
+Before reading a single parameter off this fit, one property of the replay has
+to be dealt with. The batch at step $t$ is a deterministic function of $t$ with
+period one epoch, so the data term of every stochastic gradient repeats with
+that period. The parameters and the Monte Carlo draw do not, so the realized
+gradients are not literally periodic, but the iterate inherits a component
+locked to the replay order. Two measurements separate that component from
+genuine progress: compare the same phase in consecutive epochs, and look at the
+spread within a single epoch.
+
+```{code-cell} ipython3
+mu_t = np.asarray(tail.mu)
+sd_unc = approx.std.eval()  # variational sd in the unconstrained space, before averaging
+ends = mu_t[steps_per_epoch - 1 :: steps_per_epoch]  # same phase, one epoch apart
+step = np.abs(np.diff(ends, axis=0)) / sd_unc  # (7 epoch pairs, 154 coordinates)
+last = mu_t[-steps_per_epoch:]
+swing = (last.max(0) - last.min(0)) / sd_unc
+print(
+    "same phase, epoch to epoch, max over coordinates per pair:\n  "
+    + " ".join(f"{v:.2f}" for v in step.max(1))
+    + f"\n  median over coordinates, last pair: {np.median(step[-1]):.2f} sd"
+    f"\nwithin the last epoch: median {np.median(swing):.2f}, max {swing.max():.1f} sd"
+)
+```
+
+Between the same phase of consecutive epochs the largest movement decays from about six posterior widths
+in the first pair to about half a width in the last, and the median coordinate
+moves a tenth of a width per pass; within a single epoch the same coordinates
+swing by multiples of their width. So the optimizer is not converging to a
+point. It is orbiting one, on a cycle locked to the order the rows are replayed
+in, and most of where the last iterate sits is the phase of that orbit at the
+step the budget ran out.
+
+The number you would report therefore depends on where you stop, so the fit
+is summarized by averaging the variational parameters over the final whole
+pass. To the extent the order-locked
+component repeats from one pass to the next, a window of exactly one pass
+averages it out (which is why the step budget above is a whole number of
+epochs), and a window that is not a whole number of passes leaves a residual of
+the order of one swing divided by the window length. This is Polyak–Ruppert averaging {cite:p}`polyak1992acceleration`. What the
+average does *not* remove
+is the slow drift the first line still shows: a tenth of a width per pass in
+the median, and much more along the directions the recovery table is about to
+single out.
+
+```{code-cell} ipython3
+rho_t = np.asarray(tail.rho)
+approx.params[0].set_value(mu_t[-steps_per_epoch:].mean(0))
+approx.params[1].set_value(rho_t[-steps_per_epoch:].mean(0))
+print(f"variational parameters averaged over the final {steps_per_epoch} steps (one epoch)")
+```
+
+Everything below reads from that averaged approximation.
+
++++
+
+## Did it recover the truth?
+
+```{code-cell} ipython3
+idata = approx.sample(2_000, random_seed=RANDOM_SEED)
+post = idata.posterior
+
+scalar_params = [
+    "kappa0",
+    "alpha0",
+    "lambda_a",
+    "lambda_q",
+    "beta_a",
+    "beta_q",
+    "theta_d",
+    "theta_r",
+    "nu",
+]
+rows = {name: [float(post[name].mean()), float(post[name].std())] for name in scalar_params}
+# the intercepts share a ridge with their group means; only the sums are identified
+for intercept, b in [("kappa0", "b_k"), ("alpha0", "b_al"), ("beta_a", "b_ba")]:
+    s = post[intercept] + post[b].mean("symbol")
+    rows[f"{intercept} + mean({b})"] = [float(s.mean()), float(s.std())]
+recovery = pd.DataFrame(rows, index=["mean", "sd"]).T
+recovery["truth"] = [truth[p] for p in scalar_params] + [
+    truth["kappa0"],
+    truth["alpha0"],
+    truth["beta_a"],
+]
+recovery["abs_error"] = (recovery["mean"] - recovery["truth"]).abs()
+recovery["z"] = (recovery["mean"] - recovery["truth"]) / recovery["sd"]
+recovery.round(4)
+```
+
+```{code-cell} ipython3
+# Is the table a property of the problem or of one optimizer trajectory? Three
+# more readings of the same data and budget: the notebook's own last iterate
+# before averaging; a refit with a different optimizer seed on the same replay
+# order; and a refit on a different on-disk order — the replay order is the one
+# ingredient the orbit story is about, so it is the one worth varying.
+raw_splits = ["kappa0", "alpha0", "beta_a"]
+identified = [name for name in recovery.index if name not in raw_splits]
+
+
+def summarize(post_):
+    """Posterior mean and sd of every row the recovery table reports."""
+    out = {name: (float(post_[name].mean()), float(post_[name].std())) for name in scalar_params}
+    for intercept, b in [("kappa0", "b_k"), ("alpha0", "b_al"), ("beta_a", "b_ba")]:
+        total = post_[intercept] + post_[b].mean("symbol")
+        out[f"{intercept} + mean({b})"] = (float(total.mean()), float(total.std()))
+    return pd.DataFrame(out, index=["mean", "sd"]).T
+
+
+def reshuffle(src_dir, salt):
+    """A second on-disk order: re-key the already-shuffled rows with a different salt."""
+    dst_dir = tempfile.mkdtemp(prefix="ticks_reorder_")
+    full = pa.concat_tables(
+        [pq.read_table(os.path.join(src_dir, f)) for f in sorted(os.listdir(src_dir))]
+    )
+    order2 = np.argsort(
+        splitmix64(np.arange(full.num_rows, dtype=np.uint64) + np.uint64(salt)), kind="stable"
+    )
+    for i in range(n_shards):
+        pq.write_table(
+            full.take(order2[i::n_shards]),
+            os.path.join(dst_dir, f"shard_{i:03d}.parquet"),
+            row_group_size=BATCH,
+        )
+    return dst_dir
+
+
+def refit(seed, data_loader):
+    stream_s, tail_s = StreamAdvance(model, data_loader), ParamTrace()
+    stream_s.prime()
+    with model:
+        advi_s = pm.ADVI(random_seed=seed)
+    advi_s.fit(
+        6_000, obj_optimizer=pm.adam(learning_rate=0.02), callbacks=[stream_s], progressbar=False
+    )
+    approx_s = advi_s.fit(
+        2_400,
+        obj_optimizer=pm.adam(learning_rate=0.005),
+        callbacks=[stream_s, tail_s],
+        progressbar=False,
+    )
+    approx_s.params[0].set_value(np.asarray(tail_s.mu)[-steps_per_epoch:].mean(0))
+    approx_s.params[1].set_value(np.asarray(tail_s.rho)[-steps_per_epoch:].mean(0))
+    return summarize(approx_s.sample(2_000, random_seed=seed).posterior)
+
+
+approx.params[0].set_value(mu_t[-1])
+approx.params[1].set_value(rho_t[-1])
+last_iterate = summarize(approx.sample(2_000, random_seed=RANDOM_SEED).posterior)
+approx.params[0].set_value(mu_t[-steps_per_epoch:].mean(0))
+approx.params[1].set_value(rho_t[-steps_per_epoch:].mean(0))
+
+other_dir = reshuffle(data_dir, salt=1)
+other_loader = DataLoader(
+    parquet_source(other_dir, columns=columns), batch_size=BATCH, shuffle=False, total_size="auto"
+)
+readings = {
+    "last iterate": last_iterate,
+    "seed 0": recovery[["mean", "sd"]],
+    "seed 1, same order": refit(RANDOM_SEED + 1, loader),
+    "seed 2, other order": refit(RANDOM_SEED + 2, other_loader),
+}
+tru = recovery.loc[identified, "truth"]
+z_own = {
+    k: ((v.loc[identified, "mean"] - tru) / v.loc[identified, "sd"]).abs().max()
+    for k, v in readings.items()
+}
+means = pd.DataFrame(
+    {k: v.loc[identified, "mean"] for k, v in readings.items() if k != "last iterate"}
+)
+spread = means.std(axis=1) / recovery.loc[identified, "sd"]
+print(
+    "identified rows, max |z| against the truth, each fit in its own posterior sd:\n  "
+    + "\n  ".join(f"{k}: {v:.2f}" for k, v in z_own.items())
+    + "\nspread of the mean across the three tail-averaged fits, in seed-0 sd:"
+    f"\n  median {spread.median():.2f}, max {spread.max():.2f}"
+)
+```
+
+The table is a selection, the scalar globals and the three identified sums,
+and the figure after it checks one of the five symbol-effect families; that,
+plus the two curves for one symbol further down, is the extent of the recovery
+evidence here. Read the table bottom-up. The raw intercepts look off by 0.3
+and 1.3, but a global coefficient and the mean of its group effects are only *jointly*
+pinned by the likelihood, which is exactly flat along
+$(\alpha_0 + d,\ b^{(\alpha)} - d)$. What tilts that direction at all is the
+hierarchical prior: shifting every symbol effect by $d$ costs
+$\sum_s (b_s - d)^2 / 2\tau^2$, which is minimized when the effects average
+to zero, and the generator standardized them to average exactly zero, so the
+prior points the split at the generating value. Conditional on everything
+else, that cost is a Gaussian in $d$ with standard deviation $\tau/\sqrt{12}$,
+a tenth for $\tau$ near the generator's $0.35$, which is the scale of
+uncertainty the split actually carries. The same
+translation ridge exists for every global-coefficient/group-effect pair in
+this model (including the vector pairs $c$/$b^{(\pi h)}$ and
+$g$/$b^{(\sigma h)}$, whose sums the table omits).
+
+Two things follow, and the table shows both. Along a direction that flat, a
+gradient optimizer crawls: 8,400 steps have carried `alpha0` to $-1.70$ on its
+way to $-3.00$. To see that it is still traveling, warm-start a fresh optimizer
+at the last iterate and give it sixty more passes at the same learning rate:
+
+```{code-cell} ipython3
+# Continue from the last iterate on a fresh optimizer, so `approx` and its loss
+# history above are left exactly as they were.
+with model:
+    advi_more = pm.ADVI(random_seed=RANDOM_SEED)
+advi_more.approx.params[0].set_value(mu_t[-1])
+advi_more.approx.params[1].set_value(rho_t[-1])
+stream_more = StreamAdvance(model, loader)
+stream_more.prime()
+where = {name: approx.ordering[name][1].start for name in ["kappa0", "alpha0"]}
+path = [[mu_t[-1][where["kappa0"]], mu_t[-1][where["alpha0"]]]]  # +0 passes
+
+
+class EpochMeans:
+    def __call__(self, approx_, losses, i):
+        if i % steps_per_epoch == 0:
+            mu_now = approx_.params[0].get_value()
+            path.append([mu_now[where["kappa0"]], mu_now[where["alpha0"]]])
+
+
+advi_more.fit(
+    60 * steps_per_epoch,
+    obj_optimizer=pm.adam(learning_rate=0.005),
+    callbacks=[stream_more, EpochMeans()],
+    progressbar=False,
+)
+path = np.asarray(path)
+more_loss = np.asarray(advi_more.hist) * ELBO_SCALE
+print(
+    "after +0 / +30 / +60 passes\n"
+    f"  kappa0: {' / '.join(f'{v:.3f}' for v in path[[0, 30, 60], 0])} (generating {truth['kappa0']:.3f})\n"
+    f"  alpha0: {' / '.join(f'{v:.3f}' for v in path[[0, 30, 60], 1])} (generating {truth['alpha0']:.3f})\n"
+    "  loss per pass over those 60: "
+    f"{(more_loss[-steps_per_epoch:].mean() - more_loss[:steps_per_epoch].mean()) / 59:+.2f} nats"
+)
+```
+
+The ridge coordinates keep moving toward their generating values, at a rate the
+loss barely registers. The raw split rows are a fit still traveling along a
+nearly flat direction, not an ambiguity in the model. And their reported
+widths, a few thousandths, are the mean-field *conditional* width across the ridge, not the tenth just
+computed along it: mean-field has no correlation to spend,
+so it reports the narrow direction as if it were the wide one, which is why
+`alpha0` reads $z = 379$. Both are reasons a sum-to-zero constraint on the
+symbol effects is the standard reparameterization when the split itself
+matters: it removes the direction instead of asking the optimizer to find its
+way along it.
+
+The identified rows are a different story. Every row the table reports that
+*is* pinned by the likelihood, the three sums and the six standalone
+coefficients, lands inside $1.2$ posterior standard deviations of its
+generating value, and seven of those nine are inside $0.5$. The replicate cell puts that in context from two directions.
+Read from the last iterate instead of the tail average, the same rows ran to
+$3.4$; that difference was the orbit, not the estimate. And on two refits,
+one with a different optimizer seed on the same replay order and one on a
+different on-disk order altogether, the identified rows land in the same
+range, each fit judged in its own posterior width, with the spread of the means
+across the three tail-averaged fits about a tenth of the reported width. That
+is the replication check, and what it buys is limited. Material disagreement
+between fits would have been enough to withhold any claim about a width;
+agreement only removes the instability warning. It does not validate
+calibration. The widths here are those of a mean-field approximation, and
+z-scores this small on one dataset are consistent with widths that are
+somewhat too narrow, somewhat too wide, or right; sizing that needs many
+simulated datasets, and this notebook fits one.
+
+The more interesting check is hierarchical: what happened to the per-symbol
+effects of the two symbols with 1,800 and 900 rows?
+
+```{code-cell} ipython3
+# plot the identified CONTRAST b - mean(b): the table above showed the absolute
+# level belongs to a ridge with the intercept, so comparing raw b to raw truth
+# would only display the arbitrary level split. Hand-rolled rather than
+# az.plot_forest because the display is bespoke: truth overlay + thin shading.
+bc = post["b_al"] - post["b_al"].mean("symbol")
+b_mean = bc.mean(("chain", "draw")).values
+b_sd = bc.std(("chain", "draw")).values
+b_truth = 0.35 * z_truth["z_al"]  # the generator's scale times its z draws
+b_truth = b_truth - b_truth.mean()
+
+fig, ax = plt.subplots(figsize=(8, 3.5), layout="constrained")
+x = np.arange(n_symbols)
+ax.errorbar(
+    x,
+    b_mean,
+    yerr=2 * b_sd,
+    fmt="o",
+    color="C0",
+    capsize=3,
+    label="mean ± 2 posterior sd (mean-field)",
+)
+ax.scatter(x, b_truth, marker="x", color="C1", s=60, zorder=3, label="truth")
+for t in thin:
+    ax.axvspan(t - 0.4, t + 0.4, color="C3", alpha=0.12)
+ax.set_xticks(x)
+ax.set_xticklabels([f"S{i}" for i in x])
+ax.set_xlabel("symbol (shaded = thin: 1,800 and 900 rows)")
+ax.set_ylabel(r"$b^{(\alpha)}_s - \bar{b}^{(\alpha)}$")
+ax.set_title("Symbol effects against the generating values (thin symbols shaded)")
+ax.legend()
+
+order = np.argsort(-b_sd)
+print(
+    "posterior sd of the contrast, widest first:\n  "
+    + ", ".join(f"S{i:02d} {b_sd[i]:.3f} ({counts[i]:,} rows)" for i in order[:3])
+    + f"\n  ... narrowest S{order[-1]:02d} {b_sd[order[-1]]:.3f} ({counts[order[-1]]:,} rows)"
+)
+```
+
+The posterior spread is widest for the symbols with the least data (the
+printout orders them), which is the hierarchy expressing that it
+knows less about those groups {cite:p}`gelman2006data`. The point estimates
+track their generating values across the board, including the thin ones.
+
+What the figure does *not* show is shrinkage in the strict sense: that would
+need an unpooled per-symbol fit to compare against, and this notebook does not
+run one. Nor are the bars calibrated intervals; the paragraph above declined
+to treat mean-field widths that way, and that applies to their ordering too.
+And whether pooling improves held-out prediction on real data is a separate
+question again, which nothing here tests.
+
+What do those parameters mean as *objects*? For one symbol, holding the
+covariates at $a = q = 0$, the fit implies two curves over the event clock: the
+probability that the next event leaves the price unchanged, and the 90%
+half-width of the move given that one happens. Both are built from
+global-plus-symbol sums, so neither sits on the translation ridge.
+
+```{code-cell} ipython3
+s_idx, hours = 0, np.arange(24)
+Bh = hour_basis(hours)  # (24, 4)
+# .dataset: idata.posterior is a DataTree in arviz 1.x
+st = post.dataset.stack(sample=("chain", "draw"))
+
+
+def pick(name, *dims):
+    return st[name].transpose(*dims, "sample").values
+
+
+logit_pi = (
+    pick("kappa0")[None, :]
+    + pick("b_k", "symbol")[s_idx][None, :]
+    + Bh @ (pick("c", "harmonic") + pick("b_ph", "symbol", "harmonic")[s_idx])
+)
+log_sigma = (
+    pick("alpha0")[None, :]
+    + pick("b_al", "symbol")[s_idx][None, :]
+    + Bh @ (pick("g", "harmonic") + pick("b_sh", "symbol", "harmonic")[s_idx])
+)
+# transform each draw, then summarize: the median of a function, not a function of medians
+zero_prob = np.median(1.0 / (1.0 + np.exp(logit_pi)), axis=1)
+half90 = np.median(np.exp(log_sigma) * stats.t.ppf(0.95, pick("nu"))[None, :], axis=1)
+
+t_logit = (
+    truth["kappa0"]
+    + 0.30 * z_truth["z_k"][s_idx]
+    + Bh @ (truth["c"] + 0.15 * z_truth["z_ph"][s_idx])
+)
+t_logsig = (
+    truth["alpha0"]
+    + 0.35 * z_truth["z_al"][s_idx]
+    + Bh @ (truth["g"] + 0.12 * z_truth["z_sh"][s_idx])
+)
+
+fig, axs = plt.subplots(1, 2, figsize=(9, 3.2), layout="constrained")
+for ax, post_curve, truth_curve, ylab in [
+    (axs[0], zero_prob, 1.0 / (1.0 + np.exp(t_logit)), "P(next event does not move)"),
+    (axs[1], half90, np.exp(t_logsig) * stats.t.ppf(0.95, truth["nu"]), "90% half-width (bp)"),
+]:
+    ax.plot(hours, post_curve, color="C0", lw=2, label="posterior median")
+    ax.plot(hours, truth_curve, color="C1", ls="--", lw=1.5, label="generator truth")
+    ax.set_xlabel("UTC hour")
+    ax.set_ylabel(ylab)
+axs[0].legend(fontsize=9)
+fig.suptitle(f"SYM{s_idx:02d} on the event clock, at a = q = 0", fontsize=11);
+```
+
+The half-width is a quantile of the *outcome* distribution, not a credible band
+for the curve: it answers "how far does a move go", and it stays finite for any
+$\nu > 0$. That is why the notebook reports it instead of a conditional
+standard deviation: the Student-t variance exists only for $\nu > 2$, and
+nothing in this model guarantees that. Under this generator, the fit recovers
+both shapes; on real data the same two curves would be estimates, and would
+need out-of-sample evaluation before being used for anything.
+
++++
+
+## In-sample posterior predictive adequacy
+
+A recovery table checks parameters; a posterior predictive check asks the
+model to reproduce the data features it exists to describe. The `CustomDist`
+carries a `random` implementation alongside its `logp`, so
+{func}`~pymc.sample_posterior_predictive` works out of the box. The two
+features that matter for this model are the exact-zero share and the heavy
+conditional tail; the figure after the table shows the whole empirical
+cumulative distribution function (ECDF) of one batch against sixty predictive
+draws:
+
+```{code-cell} ipython3
+eval_batch = next(iter(loader))
+model["batch"].set_value(eval_batch, borrow=True)  # same path the callback uses
+with model:
+    idata = pm.sample_posterior_predictive(
+        idata,
+        var_names=["y_obs"],
+        random_seed=RANDOM_SEED,
+        extend_inferencedata=True,
+        progressbar=False,
+    )
+
+pp = idata.posterior_predictive["y_obs"].stack(sample=("chain", "draw")).values.T
+y_eval = eval_batch[:, 0]
+
+zero_share = (pp == 0).mean(axis=1)
+med_nonzero = np.array([np.median(np.abs(d[d != 0])) for d in pp[:500]])
+q99_nonzero = np.array([np.quantile(np.abs(d[d != 0]), 0.99) for d in pp[:500]])
+
+
+def check_row(observed, draws):
+    lo, hi = np.quantile(draws, [0.05, 0.95])
+    return [observed, draws.mean(), lo, hi]
+
+
+y_nonzero = np.abs(y_eval[y_eval != 0])
+pd.DataFrame(
+    [
+        check_row((y_eval == 0).mean(), zero_share),
+        check_row(np.median(y_nonzero), med_nonzero),
+        check_row(np.quantile(y_nonzero, 0.99), q99_nonzero),
+    ],
+    columns=["observed", "predictive mean", "predictive 5%", "predictive 95%"],
+    index=["zero share", "median |move| (bp)", "q99 |move| (bp)"],
+).round(3)
+```
+
+```{code-cell} ipython3
+# hand-rolled ECDF: the bespoke feature is the annotated discrete jump at zero,
+# which the predictive must reproduce in both location and height
+fig, ax = plt.subplots(figsize=(8, 3.5), layout="constrained")
+grid = np.linspace(-0.5, 0.5, 801)
+for draw in pp[:60]:
+    ax.plot(grid, np.searchsorted(np.sort(draw), grid) / draw.size, color="C0", alpha=0.08, lw=1)
+ax.plot(
+    grid, np.searchsorted(np.sort(y_eval), grid) / y_eval.size, color="k", lw=1.6, label="observed"
+)
+ax.plot([], [], color="C0", label="posterior predictive (60 draws)")
+zero_jump = (y_eval == 0).mean()
+below = (y_eval < 0).mean()
+ax.annotate(
+    f"vertical step at exactly 0:\nthe zero share ({zero_jump:.0%})",
+    (0.03, below + zero_jump / 2),
+    fontsize=9,
+    ha="left",
+    va="center",
+)
+ax.set_xlabel("next-event return (bp)")
+ax.set_ylabel("empirical CDF")
+ax.set_title("Posterior predictive ECDF: the step at zero is the hurdle")
+ax.legend(loc="upper left");
+```
+
+All three statistics sit inside their predictive 90% bands, though the zero
+share sits close to the lower edge, 0.300 observed against a band starting at
+0.299, so it is a pass with almost no margin rather than a comfortable one.
+What that establishes is limited in two ways. The heading is literal: the
+evaluation batch was seen during the fit, so this is adequacy, not
+generalization, and a held-out split is the next thing to add before any of
+this is used to compare models. And the check
+is one batch, one step ahead, with the observed `ylag` held fixed: it does not
+simulate a price path forward, and it says nothing about the hierarchy, the
+hourly curves, or the covariate responses, each of which would need its own
+stratified check.
+
++++
+
+## What a stopping rule has to be able to see
+
+A streamed ELBO trace is noisy: every value is a one-batch, one-Monte Carlo
+estimate, so consecutive losses differ mostly because the batch changed. That
+makes "has it converged?" a signal-detection problem, and the horizon over
+which you look is the whole game.
+
+Write the standardized *block contrast* at horizon $w$: average the $w$ losses
+before a point, average the $w$ losses after it, and divide the difference by
+the noise scale of that difference,
+
+$$
+z_w(t) = \frac{\bar L_{t-2w:t-w} - \bar L_{t-w:t}}
+              {\hat\sigma \sqrt{2/w}},
+$$
+
+with $\hat\sigma$ estimated from successive differences. Positive $z_w$ means
+the loss fell. For noise without long memory, averaging divides it by
+$\sqrt{w}$ while a steady drift accumulates linearly in $w$, so the detectable
+drift shrinks like $w^{-3/2}$: the horizon does not merely smooth the picture,
+it sets what is visible at all. A fixed replay order is not noise of that kind,
+and the printout below shows where the generic scaling breaks, in the
+notebook's favor.
+
+```{code-cell} ipython3
+# Stage 2 only, on the full-data scale: after the last planned optimizer change,
+# which is where a stopping decision would actually be taken.
+stage2 = loss[6_000:]
+sigma_hat = np.mean(np.abs(np.diff(stage2))) * np.sqrt(np.pi) / 2.0
+
+
+def signed_z(losses, w):
+    """Standardised contrast between adjacent blocks of w losses."""
+    csum = np.concatenate([[0.0], np.cumsum(losses)])
+    t = np.arange(2 * w, len(losses))
+    older = (csum[t - w] - csum[t - 2 * w]) / w
+    newer = (csum[t] - csum[t - w]) / w
+    return t, (older - newer) / (sigma_hat * np.sqrt(2.0 / w))
+
+
+horizons = [(1, "per step"), (steps_per_epoch, "one epoch"), (2 * steps_per_epoch, "two epochs")]
+# control: a horizon that is NOT a whole number of passes, to test whether the
+# variance collapse below is really about epoch alignment
+control = [(steps_per_epoch + steps_per_epoch // 2, "1.5 epochs")]
+spreads = {}
+for w, label in horizons + control:
+    _, z = signed_z(stage2, w)
+    spreads[label] = z.std()
+    print(
+        f"{label:>10s} (w={w:4d}):  mean z {z.mean():+.3f}   sd {z.std():.2f}"
+        f"   |mean|/sd {abs(z.mean()) / z.std():.2f}"
+    )
+aligned = max(spreads["one epoch"], spreads["two epochs"])
+print(f"\ncontrol spread is {spreads['1.5 epochs'] / aligned:.0f}x the widest aligned spread")
+```
+
+```{code-cell} ipython3
+fig, (ax, ax_zoom) = plt.subplots(
+    1, 2, figsize=(9, 3.2), layout="constrained", gridspec_kw={"width_ratios": [3, 2]}
+)
+for (w, label), color in zip(horizons, ["C7", "C0", "C1"]):
+    t, z = signed_z(stage2, w)
+    ax.plot(
+        t,
+        z,
+        color=color,
+        lw=0.9 if w == 1 else 1.6,
+        alpha=0.5 if w == 1 else 1.0,
+        label=f"{label} (w={w})",
+    )
+    if w > 1:
+        ax_zoom.plot(t, z, color=color, lw=1.6, label=f"{label} (w={w})")
+for ax_ in (ax, ax_zoom):
+    ax_.axhline(0.0, color="k", lw=0.8)
+    ax_.set_xlabel("step within stage 2")
+ax.set_ylabel(r"block contrast $z_w$")
+ax.set_title("Same trace, three horizons", fontsize=11)
+ax.legend(fontsize=8, loc="upper right", frameon=True)
+ax_zoom.set_ylim(-0.12, 0.12)
+ax_zoom.set_title(
+    f"Aligned horizons only (control sd {spreads['1.5 epochs']:.2f} would fill this)", fontsize=10
+)
+ax_zoom.legend(fontsize=8, loc="upper right", frameon=True);
+```
+
+Read the printout as three signal-to-noise ratios, and the figure as the same
+thing twice: the left panel puts all three horizons on one scale, the right
+panel zooms in on the two that are invisible at that scale. At $w = 1$ the
+standardized contrast has unit spread (which is what the $\sqrt{2/w}$ scaling
+is built to produce) and a mean indistinguishable from zero: a single
+increment says nothing about the trend.
+
+At the two epoch-aligned horizons the spread collapses far below what
+independent noise would predict, and the control row explains why. The
+shuffle is done once on disk and replayed in the same order every epoch, so a
+window of exactly one or two passes averages over the *same rows* every time
+and the batch-composition noise, which dominates the per-step view, all but
+drops out.
+Widen the window to one and a half passes and it does not: the printed ratio
+puts the control an order of magnitude above the wider of its two neighbors,
+even though it is itself *wider* than the one-epoch window. Nothing but the
+alignment changed.
+
+What survives is Monte Carlo noise and the parameter drift
+itself, and against that much smaller yardstick the drift becomes visible:
+the ratio in the last column rises with the horizon. So, with a fixed replay
+order, the useful horizons are the ones commensurate with an epoch. That is a
+property of how the data was shuffled, not of the optimizer.
+
+:::{admonition} Why a naive rule fires instead of staying silent
+:class: warning
+It is tempting to accumulate per-step evidence with a one-sided cumulative-sum
+statistic of the kind {cite:t}`page1954continuous` introduced, here adapted to
+the standardized improvement: $S \leftarrow \max(0,\ S + (\kappa -
+\max(z, 0)))$, stopping when $S$ exceeds a threshold. Read carefully, that
+recursion behaves sensibly while there is signal: if the standardized
+improvement stays above $\kappa$, the increment is negative and $S$ sits at
+zero. The difficulty is what happens when there is *no* resolvable signal.
+Under symmetric noise $\mathbb{E}[\max(z,0)]$ is only a fraction of a standard
+deviation, so with $\kappa$ above that value $S$ climbs at a roughly constant
+rate and crosses any fixed threshold after a roughly fixed number of steps. The
+rule therefore cannot tell "converged" from "still improving, but too slowly
+for this horizon to see"; it announces the same thing at the same pace in
+both cases. So the fix is not a better threshold but a wider horizon, plus a
+second yardstick that asks whether the remaining improvement is negligible
+relative to the reduction already achieved. That design is implemented in
+[pymc-extras#733](https://github.com/pymc-devs/pymc-extras/pull/733); this
+notebook shows the observation problem it exists to solve rather than shipping
+a second copy of it.
+:::
+
+How much would an online rule be worth here? The retrospective answer is a
+benchmark you can only compute afterwards, which is why the online version is
+needed:
+
+```{code-cell} ipython3
+# t99: the first step at which a trailing average of width one epoch has covered
+# 99% of stage 2's total smoothed reduction. Retrospective by construction.
+kernel = np.ones(steps_per_epoch) / steps_per_epoch
+smoothed = np.convolve(stage2, kernel, mode="valid")
+total_drop = smoothed[0] - smoothed.min()
+# "valid" convolution starts at the first full window, so index j of `smoothed`
+# is the trailing average ending at stage-2 step j + steps_per_epoch, counting
+# steps from 1.
+t99 = int(np.argmax(smoothed <= smoothed.min() + 0.01 * total_drop)) + steps_per_epoch
+print(
+    f"stage 2 smoothed reduction: {total_drop:,.2f} nats;\n"
+    f"99% of it reached by step {t99:,} of {len(stage2):,} "
+    f"({t99 / len(stage2):.0%} of the stage-2 budget)"
+)
+```
+
+Any stopping rule, this one included, comes with two qualifications. What such a rule
+detects is a *loss plateau*, a necessary signal, not a proof that the
+posterior has converged; the recovery and predictive checks above are the kind
+of independent evidence a stop should be paired with. And a plateau in a noisy
+loss can only ever be established relative to a horizon: at any finite step, an
+improvement small enough is indistinguishable from none.
+
+## Related tooling in pymc-extras
+
+The loader used above is one of four streaming pieces in pymc-extras. The
+others are not imported here; this is where each one fits.
+
+| Component | What it does |
+| --- | --- |
+| [`DataLoader` / `parquet_source`](https://github.com/pymc-devs/pymc-extras/pull/698) | Turns an out-of-core source into minibatches and owns `total_size`; used directly above |
+| [`Trainer`](https://github.com/pymc-devs/pymc-extras/pull/710) | Wraps the data-advance lifecycle around `pm.fit`, the job the twenty-line `StreamAdvance` does here |
+| [`CheckLossConvergence`](https://github.com/pymc-devs/pymc-extras/pull/733) | Loss-based stopping on growing block horizons with two yardsticks; the observation problem it addresses is the previous section |
+| [streaming Pathfinder](https://github.com/pymc-devs/pymc-extras/pull/722) | A short quasi-Newton run on minibatch gradients that returns a Gaussian proposal, importance-corrected against the full-data log-density, with Pareto-$k$ as its own veto |
+
+Pathfinder is the faster route when an approximately placed starting point is
+what you need, for example to initialize Markov chain Monte Carlo (MCMC). It is not run here, and the
+reason is a decision taken before fitting rather than a result: its documented
+operating range is non-hierarchical targets of at most a few tens of
+parameters, and this target is hierarchical with
+
+```{code-cell} ipython3
+n_free = DictToArrayBijection.map(model.initial_point()).data.size
+print(f"free parameters in the unconstrained space: {n_free}")
+```
+
+free parameters, an order of magnitude outside that range. The ADVI results
+above stand or fall on their own recovery and predictive checks, independently
+of this choice.
+
++++
+
+## Acknowledgements
+
+This notebook was written as part of the 2026
+[Google Summer of Code](https://summerofcode.withgoogle.com/) project
+*Streaming Variational Inference for Large Datasets* with PyMC and
+[NumFOCUS](https://numfocus.org/), mentored by Rob Zinkov and Chris Fonnesbeck.
+
++++
+
+## Authors
+
+* Authored by [Yicheng Yang](https://github.com/YichengYang-Ethan) in August
+  2026 ([pymc-examples#892](https://github.com/pymc-devs/pymc-examples/pull/892))
+
++++
+
+## References
+
+:::{bibliography}
+:filter: docname in docnames
+:::
+
++++
+
+## Watermark
+
+```{code-cell} ipython3
+%load_ext watermark
+print(f"wall time for every cell above, on this machine: {time.perf_counter() - NOTEBOOK_T0:.0f} s")
+%watermark -n -u -v -iv -w -p xarray
+%watermark -m
+```
+
+:::{include} ../page_footer.md
+:::