Blackjax's Sequential Monte Carlo over PyMC models. #6989
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Additional details and impacted files@@ Coverage Diff @@
## main #6989 +/- ##
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- Coverage 87.78% 86.87% -0.91%
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Files 100 102 +2
Lines 16896 17017 +121
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- Hits 14832 14784 -48
- Misses 2064 2233 +169
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junpenglao
reviewed
Nov 4, 2023
| model = fast_model() | ||
| population = {"x": np.array([2, 3, 4])} | ||
| blackjax_particles = blackjax_particles_from_pymc_population(model, population) | ||
| chex.assert_trees_all_close(blackjax_particles, [np.array([[2], [3], [4]])]) |
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I dont think PyMC use chex - use jax.tree_map(np.testing.assert..., ...) instead
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looks like the dependency is transitive (pymc -> blackjax -> optax -> chex)
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Can we add this to pymc-experimental first? |
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Closing this one in favour of pymc-devs/pymc-extras#267 |
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What is this PR about?
Allows to sample Pymc's models using Blackjax's Sequential Monte Carlo implementations. Apart from getting a Jax-based implementation for Blackjax, this PR allows for using HMC and NUTS as kernels, which aren't available in the existing PyMC implementation of SMC. Moreover, diagnosis are exposed and stored in the resulting arviz.InferenceData
Checklist
In order to sample using BJ SMC, we need
Major / Breaking Changes
None
New features
Bugfixes
Documentation
Maintenance
@junpenglao @aloctavodia