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In order to construct accurate proposers for Metropolis-Hastings Markov ChainMonte Carlo, we integrate ideas from probabilistic graphical models and neuralnetworks in an open-source framework we call Lightweight Inference Compilation(LIC). LIC implements amortized inference within an open-universe declarativeprobabilistic programming language (PPL). Graph neural networks are used toparameterize proposal distributions as functions of Markov blankets, whichduring "compilation" are optimized to approximate single-site Gibbs samplingdistributions. Unlike prior work in inference compilation (IC), LIC forgoesimportance sampling of linear execution traces in favor of operating directlyon Bayesian networks. Through using a declarative PPL, the Markov blankets ofnodes (which may be non-static) are queried at inference-time to produceproposers Experimental results show LIC can produce proposers which have lessparameters, greater robustness to nuisance random variables, and improvedposterior sampling in a Bayesian logistic regression and $n$-schools inferenceapplication.

Accelerating Metropolis-Hastings with Lightweight Inference Compilation