Towards verified stochastic variational inference for probabilistic programs

Probabilistic programming is the idea of writing models from statistics and machine learning using program notations and reasoning about these models using generic inference engines. Recently its combination with deep learning has been explored intensely, which led to the development of so called de...

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Bibliographic Details

Published in:	Proceedings of ACM on programming languages Vol. 4; no. POPL; pp. 1 - 33
Main Authors:	Lee, Wonyeol, Yu, Hangyeol, Rival, Xavier, Yang, Hongseok
Format:	Journal Article
Language:	English
Published:	ACM 01.01.2020
Subjects:	Computer Science Programming Languages Probabilistic computation Variational methods Probabilistic programming Semantics Denotational semantics Static analysis Machine learning Bayesian computation Correctness Automated static analysis
ISSN:	2475-1421, 2475-1421
Online Access:	Get full text
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