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Mean-field variational approximate Bayesian inference for latent variable models

Consonni, Guido; Marin, Jean-Michel (2007), Mean-field variational approximate Bayesian inference for latent variable models, Computational Statistics and Data Analysis, 52, 2, p. 790-798. http://dx.doi.org/10.1016/j.csda.2006.10.028

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Type
Article accepté pour publication ou publié
Date
2007
Journal name
Computational Statistics and Data Analysis
Volume
52
Number
2
Publisher
Elsevier
Pages
790-798
Publication identifier
Metadata
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Author(s)
Consonni, Guido
Marin, Jean-Michel cc
Abstract (EN)
The ill-posed nature of missing variable models offers a challenging testing ground for new computational techniques. This is the case for the mean-field variational Bayesian inference. The behavior of this approach in the setting of the Bayesian probit model is illustrated. It is shown that the mean-field variational method always underestimates the posterior variance and, that, for small sample sizes, the mean-field variational approximation to the posterior location could be poor.
Subjects / Keywords
Bayesian inference; Bayesian probit model; Gibbs sampling; Latent variable models; Marginal distribution; Mean-field variational methods