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Hierarchical Bayesian Analysis of Changepoint Problems

Bradley P. Carlin, Alan E. Gelfand and Adrian F. M. Smith
Journal of the Royal Statistical Society. Series C (Applied Statistics)
Vol. 41, No. 2 (1992), pp. 389-405
Published by: Wiley for the Royal Statistical Society
DOI: 10.2307/2347570
Stable URL: http://www.jstor.org/stable/2347570
Page Count: 17
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Hierarchical Bayesian Analysis of Changepoint Problems
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Abstract

A general approach to hierarchical Bayes changepoint models is presented. In particular, desired marginal posterior densities are obtained utilizing the Gibbs sampler, an iterative Monte Carlo method. This approach avoids sophisticated analytic and numerical high dimensional integration procedures. We include an application to changing regressions, changing Poisson processes and changing Markov chains. Within these contexts we handle several previously inaccessible problems.

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