Implement Resamper for interactive prior tuning #3118
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fritzo
commented
Jul 19, 2022
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| # | |||
| # This is also used if you do content translation via gettext catalogs. | |||
| # Usually you set "language" from the command line for these cases. | |||
| language = None | |||
| language = "en" | |||
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this silences a new sphinx warning
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happy to review but will need a walk-through |
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Check out this pull request on See visual diffs & provide feedback on Jupyter Notebooks. Powered by ReviewNB |
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@martinjankowiak sure I'd be happy to walk you through over zoom some evening. I've also added a notebook if you want to run that locally. The algorithm is cute, but the interface seems awkward, lying somewhere between Distributions and models. Advice welcome. |
fritzo
changed the title
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ok, I've simplified and improved the interface to use a more idiomatic (model,guide) pair. |
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notebooks typos:
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martinjankowiak
previously approved these changes
Jul 25, 2022
martinjankowiak
approved these changes
Jul 25, 2022
OlaRonning
pushed a commit
to aleatory-science/pyro
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Aug 2, 2022
* Implement ResamplingCache * Vectorize for speed * lint * optimization nit * More optimization * Completely vectorize * Generalize to multiple distributions * Add a tutorial * Refactor to use models * Simplified, but introduced a bug 😕 * fix bug * Update tutorial * Implement stable sampling via Gumbel-max trick * Change nomenclature * Update prior_predictive.ipynb Fix colab link * Install pyro-ppl in colab * Address review comments
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This implements an importance resampler as a computational aid for interactively tuning prior predictive samples early in ones Bayesian workflow.
Additional work
Tested
new tests complete in <0.1sec on my machine
Open in Colab, you'll need to replace the pip install line with: