Random walk, random method #3682
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Check out this pull request on You'll be able to see Jupyter notebook diff and discuss changes. Powered by ReviewNB. |
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Note that the new data, which ships with the library, is 128kb, compared with the current 14kb. I can zip it (which pandas can read just fine) to get that down to 48kb. The final image in the notebook now looks like this, in case anyone is interested in what the last 11 years have looked like. I have also put the old final image below for comparison:
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Could you try to modify the prior a bit more to get rid of the rhat warning? |
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Yeah, I would swap samples and tuning, and take 1000 after 2000 burnin (that's a pretty reasonable default for a lot of models). |
Codecov Report
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## master #3682 +/- ##
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+ Coverage 89.82% 89.89% +0.07%
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Files 134 134
Lines 20133 20166 +33
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+ Hits 18084 18129 +45
+ Misses 2049 2037 -12
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Sampling works now (2000 draw, 2000 tune -- it works ~usually with 1000/1000 but I wanted to do it without just picking the "right" random seed). Also updated the shape handling if you really want a vectorized mu or sd. |
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Hoping to merge this tonight if there are no further suggestions! |
pymc3/distributions/timeseries.py
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| def _random(self, sigma, mu, size): | ||
| """Implement a Gaussian random walk as a cumulative sum of normals.""" | ||
| rv = stats.norm(mu, sigma) | ||
| return rv.rvs(size).cumsum(axis=0) |
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Careful with axis=0. If mu is an RV, its drawn value will have the size prepend, and that will shift the time series axis further to the right.
Was updating the stochastic volatility notebook, and wanted to sample from the prior predictive for Gaussian Random Walk. This PR: