期刊
COMPUTATIONAL STATISTICS & DATA ANALYSIS
卷 52, 期 2, 页码 790-798出版社
ELSEVIER SCIENCE BV
DOI: 10.1016/j.csda.2006.10.028
关键词
bayesian inference; Bayesian probit model; Gibbs sampling; latent variable models; marginal distribution; mean-field variational methods
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. (c) 2006 Elsevier B.V. All rights reserved.
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