Accelerating Metropolis-Hastings with Lightweight Inference Compilation

Feynman Liang · Nimar Arora · Nazanin Tehrani · Yucen Li · Michael Tingley · Erik Meijer

Keywords: [ Probabilistic Methods ] [ Probabilistic Programming ]

[ Abstract ]
Tue 13 Apr 2 p.m. PDT — 4 p.m. PDT


In order to construct accurate proposers for Metropolis-Hastings Markov Chain Monte Carlo, we integrate ideas from probabilistic graphical models and neural networks in an open-source framework we call Lightweight Inference Compilation (LIC). LIC implements amortized inference within an open-universe declarative probabilistic programming language (PPL). Graph neural networks are used to parameterize proposal distributions as functions of Markov blankets, which during ``compilation'' are optimized to approximate single-site Gibbs sampling distributions. Unlike prior work in inference compilation (IC), LIC forgoes importance sampling of linear execution traces in favor of operating directly on Bayesian networks. Through using a declarative PPL, the Markov blankets of nodes (which may be non-static) are queried at inference-time to produce proposers Experimental results show LIC can produce proposers which have less parameters, greater robustness to nuisance random variables, and improved posterior sampling in a Bayesian logistic regression and n-schools inference application.

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