Accelerating Metropolis-Hastings with Lightweight Inference Compilation

Lightweight Inference Compilation (LIC) implements amortized inference within an open-universe declarative probabilistic programming language, using a neural network to construct Metropolis-Hastings proposals that leverage the Markov blanket structure of the model; it achieves lower Kullback-Leibler divergence and faster mixing than prior inference-compilation and hand-tuned MCMC approaches.