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Bayesian auxiliary variable models for binary and multinomial regression

Abstract:

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 ho...

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Publisher copy:
10.1214/06-BA105

Authors


Holmes, CC More by this author
Journal:
Bayesian Analysis
Volume:
1
Issue:
1 A
Pages:
145-168
Publication date:
2006
DOI:
EISSN:
1931-6690
ISSN:
1936-0975
URN:
uuid:836d1da8-0202-4c8c-a229-32b80d6fe742
Source identifiers:
355224
Local pid:
pubs:355224

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