Automated Variational Inference in Probabilistic Programming

David Wingate; Theophane Weber

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arXiv · 1301.1299

Automated Variational Inference in Probabilistic Programming

David Wingate·Theophane Weber

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Abstract

We present a new algorithm for approximate inference in probabilistic programs, based on a stochastic gradient for variational programs. This method is efficient without restrictions on the probabilistic program; it is particularly practical for distributions which are not analytically tractable, including highly structured distributions that arise in probabilistic programs. We show how to automatically derive mean-field probabilistic programs and optimize them, and demonstrate that our perspective improves inference efficiency over other algorithms.

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David Wingate, Theophane Weber. 2013-01-07. Automated Variational Inference in Probabilistic Programming. https://arxiv.org/abs/1301.1299

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