Bayesian auxiliary variable models for binary and multinomial regression

Bayesian auxiliary variable models for binary and multinomial regression

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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 conve...

Полное описание

Библиографические подробности
Главные авторы:	Holmes, C, Held, L
Формат:	Journal article
Язык:	English
Опубликовано:	2006

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