Correctness of Sequential Monte Carlo Inference for Probabilistic Programming Languages

Probabilistic programming is an approach to reasoning under uncertainty by encoding inference problems as programs. In order to solve these inference problems, probabilistic programming languages (PPLs) employ different inference algorithms, such as sequential Monte Carlo (SMC), Markov chain Monte C...

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Bibliographic Details

Published in:	Programming Languages and Systems Vol. 12648; p. 404
Main Authors:	Lunden, Daniel, Borgström, Johannes, Broman, David
Format:	Book Chapter Conference Proceeding
Language:	English
Published:	Switzerland Springer International Publishing AG 01.01.2021
Series:	Lecture Notes in Computer Science
Subjects:	Functional Programming Measure Theory Operational Semantics Probabilistic Programming Sequential Monte Carlo
ISBN:	3030720187, 9783030720186, 3030720195, 9783030720193
Online Access:	Get full text
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