Stochastic Annealing for Variational Inference
Machine Learning
2015-05-26 v1
Abstract
We empirically evaluate a stochastic annealing strategy for Bayesian posterior optimization with variational inference. Variational inference is a deterministic approach to approximate posterior inference in Bayesian models in which a typically non-convex objective function is locally optimized over the parameters of the approximating distribution. We investigate an annealing method for optimizing this objective with the aim of finding a better local optimal solution and compare with deterministic annealing methods and no annealing. We show that stochastic annealing can provide clear improvement on the GMM and HMM, while performance on LDA tends to favor deterministic annealing methods.
Cite
@article{arxiv.1505.06723,
title = {Stochastic Annealing for Variational Inference},
author = {San Gultekin and Aonan Zhang and John Paisley},
journal= {arXiv preprint arXiv:1505.06723},
year = {2015}
}