R2: An Efficient MCMC Sampler for Probabilistic Programs

Aditya V. Nori, Chung-Kil Hur, Sriram K. Rajamani, and Selva Samuel

Abstract

We present a new Markov Chain Monte Carlo (MCMC) sampling algorithm for probabilistic programs. Our approach and tool, called R2, has the unique feature of employing program analysis in order to improve the efficiency of MCMC sampling. Given an input program P, R2 propagates observations in P backwards to obtain a semantically equivalent program P0 in which every probabilistic assignment is immediately followed by an observe statement. Inference is performed by a suitably modified version of the Metropolis-Hastings algorithm that exploits the structure of the program P0. This has the overall effect of preventing rejections due to program executions that fail to satisfy observations in P. We formalize the semantics of probabilistic programs and rigorously prove the correctness of R2.We also empirically demonstrate the effectiveness of R2—in particular, we show that R2 is able to produce results of similar quality as the CHURCH and STAN probabilistic programming tools with much shorter execution time.

Details

Publication typeInproceedings
Published inAAAI Conference on Artificial Intelligence (AAAI)
PublisherAAAI
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