  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.
