Bayesian Inference Using Data Flow Analysis

Guillaume Claret, Sriram Rajamani, Aditya Nori, Andy Gordon, Johannes Borgström

ESEC/FSE 2013 Proceedings of the 2013 9th Joint Meeting on Foundations of Software Engineering |

Published by ACM

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We present a new algorithm for Bayesian inference over probabilistic programs, based on data flow analysis techniques from the program analysis community. Unlike existing techniques for Bayesian inference on probabilistic programs, our data flow analysis algorithm is able to perform inference directly on probabilistic programs with loops. Even for loop-free programs, we show that data flow analysis offers better precision and better performance benefits over existing techniques. We also describe heuristics that are crucial for our inference to scale, and present an empirical evaluation of our algorithm over a range of benchmarks.