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## Commutative Semantics for Probabilistic Programming

Resource type

Authors/contributors

- Yang, Hongseok (Editor)
- Staton, Sam (Author)

Title

Commutative Semantics for Probabilistic Programming

Abstract

We show that a measure-based denotational semantics for probabilistic programming is commutative. The idea underlying probabilistic programming languages (Anglican, Church, Hakaru, ...) is that programs express statistical models as a combination of prior distributions and likelihood of observations. The product of prior and likelihood is an unnormalized posterior distribution, and the inference problem is to ﬁnd the normalizing constant. One common semantic perspective is thus that a probabilistic program is understood as an unnormalized posterior measure, in the sense of measure theory, and the normalizing constant is the measure of the entire semantic domain.

Book Title

Programming Languages and Systems

Volume

10201

Place

Berlin, Heidelberg

Publisher

Springer Berlin Heidelberg

Date

2017

Pages

855-879

Language

en

ISBN

978-3-662-54433-4 978-3-662-54434-1

Accessed

2019-11-23T16:35:50Z

Library Catalog

Crossref

Extra

ZSCC: NoCitationData[s0] DOI: 10.1007/978-3-662-54434-1_32

Citation

Staton, S. (2017). Commutative Semantics for Probabilistic Programming. In H. Yang (Ed.),

*Programming Languages and Systems*(Vol. 10201, pp. 855–879). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-662-54434-1_32
PROBABILITY & STATISTICS

PROGRAMMING LANGUAGES

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