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Holmes, C
Knorr-Held, L
Year of Publication: 
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Discussion papers / Sonderforschungsbereich 386 der Ludwig-Maximilians-Universität München 306
In this paper we highlight a data augmentation approach to inference in the Bayesian logistic regression model. We demonstrate that the resulting conditional likelihood of the regression coefficients is multivariate normal, equivalent to a standard Bayesian linear regression, which allows for efficient simulation using a block Gibbs sampler. We illustrate that the method is particularly suited to problems in covariate set uncertainty and random effects models
Auxiliary variables
Bayesian logistic regression
Data augmentation
Markov chain Monte Carlo
Model averaging
Random effects
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Working Paper

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