Abstract
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.
Lingua originale | English |
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pagine (da-a) | 790-798 |
Numero di pagine | 9 |
Rivista | COMPUTATIONAL STATISTICS & DATA ANALYSIS |
Volume | 52 |
DOI | |
Stato di pubblicazione | Pubblicato - 2007 |
Keywords
- Latent variable model
- Mean-field variational method