Variational Bayes With Intractable Likelihood. Issue 4 (2nd October 2017)

• Record Type: Journal Article
• Authors: Tran, Minh-Ngoc; Nott, David J.; Kohn, Robert
• Is Part Of: Journal of computational and graphical statistics. Volume 26:Issue 4(2017)
• Page Start: 873
• Page End: 882
• Publication Date: 2017-10-02
• Subjects: Approximate Bayesian computation -- Marginal likelihood -- Natural gradient -- Quasi-Monte Carlo -- State--space models -- Stochastic optimization; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2017.1330205
• Languages: English
• ISSNs: 1061-8600

Abstract

ABSTRACT: Variational Bayes (VB) is rapidly becoming a popular tool for Bayesian inference in statistical modeling. However, the existing VB algorithms are restricted to cases where the likelihood is tractable, which precludes their use in many interesting situations such as in state--space models and in approximate Bayesian computation (ABC), where application of VB methods was previously impossible. This article extends the scope of application of VB to cases where the likelihood is intractable, but can be estimated unbiasedly. The proposed VB method therefore makes it possible to carry out Bayesian inference in many statistical applications, including state--space models and ABC. The method is generic in the sense that it can be applied to almost all statistical models without requiring too much model-based derivation, which is a drawback of many existing VB algorithms. We also show how the proposed method can be used to obtain highly accurate VB approximations of marginal posterior distributions. Supplementary material for this article is available online.