Mean field variational bayes for elaborate distributions

Publication Type:
Journal Article
Citation:
Bayesian Analysis, 2011, 6 (4), pp. 847 - 900
Issue Date:
2011-12-01
Full metadata record
Files in This Item:
Filename Description Size
Thumbnail2010005189OK.pdf805.19 kB
Adobe PDF
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. © 2011 International Society for Bayesian Analysis.
Please use this identifier to cite or link to this item: