Bayesian Analysis

Mean Field Variational Bayes for Elaborate Distributions

Matthew P. Wand, John T. Ormerod, Simone A. Padoan, and Rudolf Frühwirth

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Abstract

We develop strategies for mean field variational Bayes approximate inference for Bayesian hierarchical models containing elaborate distributions. We loosely define elaborate distributions to be those having more complicated forms compared with common distributions such as those in the Normal and Gamma families. Examples are Asymmetric Laplace, Skew Normal and Generalized Extreme Value distributions. Such models suffer from the difficulty that the parameter updates do not admit closed form solutions. We circumvent this problem through a combination of (a) specially tailored auxiliary variables, (b) univariate quadrature schemes and (c) finite mixture approximations of troublesome density functions. An accuracy assessment is conducted and the new methodology is illustrated in an application.

Article information

Source
Bayesian Anal. Volume 6, Number 4 (2011), 847-900.

Dates
First available in Project Euclid: 13 June 2012

Permanent link to this document
https://projecteuclid.org/euclid.ba/1339616546

Digital Object Identifier
doi:10.1214/11-BA631

Mathematical Reviews number (MathSciNet)
MR2869967

Zentralblatt MATH identifier
1330.62158

Keywords
Auxiliary mixture sampling Bayesian inference Quadrature Variational methods

Citation

Wand, Matthew P.; Ormerod, John T.; Padoan, Simone A.; Frühwirth, Rudolf. Mean Field Variational Bayes for Elaborate Distributions. Bayesian Anal. 6 (2011), no. 4, 847--900. doi:10.1214/11-BA631. https://projecteuclid.org/euclid.ba/1339616546


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