Automated Variational Inference in Probabilistic Programming
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.
- Publication:
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arXiv e-prints
- Pub Date:
- January 2013
- DOI:
- 10.48550/arXiv.1301.1299
- arXiv:
- arXiv:1301.1299
- Bibcode:
- 2013arXiv1301.1299W
- Keywords:
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- Statistics - Machine Learning;
- Computer Science - Artificial Intelligence;
- Computer Science - Machine Learning