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March 2006 Bayesian auxiliary variable models for binary and multinomial regression
Leonhard Held, Chris C. Holmes
Bayesian Anal. 1(1): 145-168 (March 2006). DOI: 10.1214/06-BA105


In this paper we discuss auxiliary variable approaches to Bayesian binary and multinomial regression. These approaches are ideally suited to automated Markov chain Monte Carlo simulation. In the first part we describe a simple technique using joint updating that improves the performance of the conventional probit regression algorithm. In the second part we discuss auxiliary variable methods for inference in Bayesian logistic regression, including covariate set uncertainty. Finally, we show how the logistic method is easily extended to multinomial regression models. All of the algorithms are fully automatic with no user set parameters and no necessary Metropolis-Hastings accept/reject steps.


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Leonhard Held. Chris C. Holmes. "Bayesian auxiliary variable models for binary and multinomial regression." Bayesian Anal. 1 (1) 145 - 168, March 2006.


Published: March 2006
First available in Project Euclid: 22 June 2012

zbMATH: 1331.62142
MathSciNet: MR2227368
Digital Object Identifier: 10.1214/06-BA105

Keywords: auxiliary variables , Bayesian binary and multinomial regression , Markov chain Monte Carlo , model averaging , Scale mixture of normals , Variable selection

Rights: Copyright © 2006 International Society for Bayesian Analysis


Vol.1 • No. 1 • March 2006
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