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Logic/Constraint Programming and Concurrency: The Hard-Won Lessons of the Fifth Generation Computer Project Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Probabilistic Inference by Program Transformation in Hakaru (System Description)

Authors : Praveen Narayanan, Jacques Carette, Wren Romano, Chung-chieh Shan, Robert Zinkov

Published in: Functional and Logic Programming

Publisher: Springer International Publishing

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Abstract

We present Hakaru, a new probabilistic programming system that allows composable reuse of distributions, queries, and inference algorithms, all expressed in a single language of measures. The system implements two automatic and semantics-preserving program transformations—disintegration, which calculates conditional distributions, and simplification, which subsumes exact inference by computer algebra. We show how these features work together by describing the ideal workflow of a Hakaru user on two small problems. We highlight our composition of transformations and types in design and implementation.

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previous chapter The Boolean Constraint Solver of SWI-Prolog (System Description)
next chapter An Interaction Net Encoding of Gödel’s System 
Footnotes
1
We implement this universe of types by defining our own Haskell kind using GHC’s -XDataKinds extension. Thus, we call Hakaru and * both “universes” and “kinds”.
 
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Metadata
Title
Probabilistic Inference by Program Transformation in Hakaru (System Description)
Authors
Praveen Narayanan
Jacques Carette
Wren Romano
Chung-chieh Shan
Robert Zinkov
Copyright Year
2016
Book
Functional and Logic Programming

Print ISBN: 978-3-319-29603-6

Electronic ISBN: 978-3-319-29604-3

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-29604-3_5

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