Stochastic Annealing for Variational Inference

2015/05/25 by San Gultekin, Aonan Zhang, Gultekin, San +3
Computer Science · Mathematics · #Bayesian Methods and Mixture Models #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (stat.ML) #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.1505.06723

openalex publication_date 2015/05/25 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

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.

Citations

• A tutorial on hidden Markov models and selected applications in speech recognition
• Thermodynamical approach to the traveling salesman problem: An efficient simulation algorithm

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