Smoothness Analysis for Probabilistic Programs with Application to Optimised Variational Inference. (arXiv:2208.10530v1 [cs.PL])

We present a static analysis for discovering differentiable or more generally
smooth parts of a given probabilistic program, and show how the analysis can be
used to improve the pathwise gradient estimator, one of the most popular
methods for posterior inference and model learning. Our improvement increases
the scope of the estimator from differentiable models to non-differentiable
ones without requiring manual intervention of the user; the improved estimator
automatically identifies differentiable parts of a given probabilistic program
using our static …

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