Learning Proposals for Probabilistic Programs with Inference Combinators

We develop operators for construction of proposals in probabilistic programs, which we refer to as inference combinators. Inference combinators define a grammar over importance samplers that compose primitive operations such as application of a transition kernel and importance resampling. Proposals...

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Bibliographic Details
Published in:arXiv.org 2021-06
Main Authors: Stites, Sam, Zimmermann, Heiko, Wu, Hao, Sennesh, Eli, Jan-Willem van de Meent
Format: Article
Language:English
Subjects:
Neural networks
Probabilistic inference
Probability theory
Proposals
Resampling
Samplers
Variational methods
Online Access:Get full text
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Summary:We develop operators for construction of proposals in probabilistic programs, which we refer to as inference combinators. Inference combinators define a grammar over importance samplers that compose primitive operations such as application of a transition kernel and importance resampling. Proposals in these samplers can be parameterized using neural networks, which in turn can be trained by optimizing variational objectives. The result is a framework for user-programmable variational methods that are correct by construction and can be tailored to specific models. We demonstrate the flexibility of this framework by implementing advanced variational methods based on amortized Gibbs sampling and annealing.
ISSN:2331-8422