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Bayesian Inference Using Data Flow Analysis
Microsoft Research.
Microsoft Research.
Microsoft Research.
Microsoft Research, Cambridge.
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2013 (English)In: ESEC/FSE '13: Proceedings of the 9th joint meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering / [ed] Bertrand Meyer, Luciano Baresi, and Mira Mezini, New York, NY, USA: ACM Press, 2013, 92-102 p.Conference paper, Published paper (Refereed)
Abstract [en]

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.

Place, publisher, year, edition, pages
New York, NY, USA: ACM Press, 2013. 92-102 p.
Keyword [en]
probabilistic programming, algebraic decision diagrams, data flow analysis
National Category
Computer Science
Research subject
Computing Science
Identifiers
URN: urn:nbn:se:uu:diva-200547DOI: 10.1145/2491411.2491423ISBN: 978-1-4503-2237-9 (print)OAI: oai:DiVA.org:uu-200547DiVA: diva2:635297
Conference
9th joint meeting of the European Software Engineering Conference and the ACM SIGSOFT Symposium on the Foundations of Software Engineering
Projects
UPMARC
Available from: 2013-07-03 Created: 2013-05-30 Last updated: 2013-10-16Bibliographically approved

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Borgström, Johannes

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