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

Computational Statistics and Data Analysis

Volumn 55, Issue 1, 2011, Pages 641-643

  Armagan, Artin ^a   Zaretzki, Russell L ^b  

^a Duke University   (United States)

^b UNIVERSITY OF TENNESSEE   (United States)

Author keywords

Bayesian probit model; Gibbs sampling; Variational inference

Indexed keywords

COMPUTATIONAL METHODS; DATA HANDLING;

BAYESIAN; BAYESIAN INFERENCE; GIBBS SAMPLING; MEAN FIELD; POINT ESTIMATION; PROBIT MODELS; VARIATIONAL APPROXIMATION; VARIATIONAL INFERENCE;

BAYESIAN NETWORKS;

EID: 77958031669     PISSN: 01679473     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.csda.2010.06.005     Document Type: Article

References (5)

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2. Consonni, G., Marin, J.-M., 2007. Mean-field variational approximate Bayesian inference for latent variable models. Computational Statistics & Data Analysis 52 (2), 790-798.
3. Figueiredo, M.A.T., 2003. Adaptive sparseness for supervised learning. IEEE Transactions on Pattern Analysis and Machine Intelligence 25, 1150-1159.
4. Jaakkola, T.S., Jordan, M.I., 2000. Bayesian parameter estimation via variational methods. Statistics and Computing 10 (1), 25-37.
5. Jordan, M.I., Ghahramani, Z., Jaakkola, T.S., Saul, L.K., 1999. An Introduction to Variational Methods for Graphical Models. 105-161.