Automated Variational Inference in Probabilistic Programming
Machine Learning
2013-01-08 v1 Artificial Intelligence
Machine Learning
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.
Cite
@article{arxiv.1301.1299,
title = {Automated Variational Inference in Probabilistic Programming},
author = {David Wingate and Theophane Weber},
journal= {arXiv preprint arXiv:1301.1299},
year = {2013}
}