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.

Consonni, G., Marin Jean, M., Mean–field variational approximate Bayesian inference for latent variables models, <<COMPUTATIONAL STATISTICS & DATA ANALYSIS>>, 2007; 52 (--): 790-798. [doi:10.1016/j.csda.2006.10.028] [http://hdl.handle.net/10807/10632]

Mean–field variational approximate Bayesian inference for latent variables models

Consonni, Guido;
2007

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.
2007
Inglese
Consonni, G., Marin Jean, M., Mean–field variational approximate Bayesian inference for latent variables models, <<COMPUTATIONAL STATISTICS & DATA ANALYSIS>>, 2007; 52 (--): 790-798. [doi:10.1016/j.csda.2006.10.028] [http://hdl.handle.net/10807/10632]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10807/10632
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