Bayesian inference using data flow analysis

Guillaume Claret, Sriram K. Rajamani, Aditya V. Nori, Andrew D. Gordon, Johannes Borgström

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract / Description of output

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.
Original languageEnglish
Title of host publicationJoint Meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering, ESEC/FSE'13, Saint Petersburg, Russian Federation, August 18-26, 2013
Pages92-102
Number of pages11
DOIs
Publication statusPublished - 2013

Fingerprint

Dive into the research topics of 'Bayesian inference using data flow analysis'. Together they form a unique fingerprint.

Cite this