Learning Proposals for Probabilistic Programs with Inference Combinators

• URL: http://arxiv.org/abs/2103.00668v2
• Date: Wed, 3 Mar 2021 18:47:15 GMT
• Title: Learning Proposals for Probabilistic Programs with Inference Combinators
• Authors: Sam Stites, Heiko Zimmermann, Hao Wu, Eli Sennesh, Jan-Willem van de Meent
• Abstract summary: We develop operators for construction of proposals in probabilistic programs. Proposals in inference samplers can be parameterized using neural networks. We demonstrate the flexibility of this framework by implementing advanced variational methods.
• Score: 9.227032708135617
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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