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A note on mean-field variational approximations in Bayesian probit models

Author

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  • Armagan, Artin
  • Zaretzki, Russell L.

Abstract

We correct some conclusions presented by Consonni and Marin (2007) on the performance of mean-field variational approximations to Bayesian inferences in the case of a simple probit model. We show that some of their presentations are misleading and thus their results do not fairly present the performance of such approximations in terms of point estimation under the specified model.

Suggested Citation

  • Armagan, Artin & Zaretzki, Russell L., 2011. "A note on mean-field variational approximations in Bayesian probit models," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 641-643, January.
    • Handle: RePEc:eee:csdana:v:55:y:2011:i:1:p:641-643
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    References listed on IDEAS

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    1. Consonni, Guido & Marin, Jean-Michel, 2007. "Mean-field variational approximate Bayesian inference for latent variable models," Computational Statistics & Data Analysis, Elsevier, vol. 52(2), pages 790-798, October.
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    Cited by:

    1. Salter-Townshend, Michael & Murphy, Thomas Brendan, 2013. "Variational Bayesian inference for the Latent Position Cluster Model for network data," Computational Statistics & Data Analysis, Elsevier, vol. 57(1), pages 661-671.

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