On statistical optimality of variational Bayes

Debdeep Pati, Anirban Bhattacharya, Yun Yang

Research output: Contribution to conferencePaperpeer-review

Abstract

The article addresses a long-standing open problem on the justification of using variational Bayes methods for parameter estimation. We provide general conditions for obtaining optimal risk bounds for point estimates acquired from mean-field variational Bayesian inference. The conditions pertain to the existence of certain test functions for the distance metric on the parameter space and minimal assumptions on the prior. A general recipe for verification of the conditions is outlined which is broadly applicable to existing Bayesian models with or without latent variables. As illustrations, specific applications to Latent Dirichlet Allocation and Gaussian mixture models are discussed.

Original languageEnglish (US)
Pages1579-1588
Number of pages10
StatePublished - 2018
Externally publishedYes
Event21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018 - Playa Blanca, Lanzarote, Canary Islands, Spain
Duration: Apr 9 2018Apr 11 2018

Conference

Conference21st International Conference on Artificial Intelligence and Statistics, AISTATS 2018
Country/TerritorySpain
CityPlaya Blanca, Lanzarote, Canary Islands
Period4/9/184/11/18

ASJC Scopus subject areas

  • Statistics and Probability
  • Artificial Intelligence

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