Semi-symbolic Inference for Efficient Streaming Probabilistic Programming

Eric Atkinson, Charles Yuan, Guillaume Baudart, Louis Mandel, Michael Carbin

Abstract:

A streaming probabilistic program receives a stream of observations and produces a stream of distributions
that are conditioned on these observations. Efficient inference is often possible in a streaming context using
Rao-Blackwellized particle filters (RBPFs), which exactly solve inference problems when possible and fall
back on sampling approximations when necessary. While RBPFs can be implemented by hand to provide
efficient inference, the goal of streaming probabilistic programming is to automatically generate such efficient
inference implementations given input probabilistic programs.

In this work, we propose semi-symbolic inference, a technique for executing probabilistic programs using a
runtime inference system that automatically implements Rao-Blackwellized particle filtering. To perform exact
and approximate inference together, the semi-symbolic inference system manipulates symbolic distributions
to perform exact inference when possible and falls back on approximate sampling when necessary. This
approach enables the system to implement the same RBPF a developer would write by hand. To ensure this,
we identify closed families of distributions ś such as linear-Gaussian and finite discrete models ś on which
the inference system guarantees exact inference. We have implemented the runtime inference system in the
ProbZelus streaming probabilistic programming language. Despite an average 1.6× slowdown compared to
the state of the art on existing benchmarks, our evaluation shows that speedups of 3ל87× are obtainable on
a new set of challenging benchmarks we have designed to exploit closed families.