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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
Stable URL: http://www.jstor.org/stable/2347570
Page Count: 17
You can always find the topics here!Topics: Statism, Statistical estimation, Markov chains, Density estimation, Bayesian analysis, Random variables, Datasets, Poisson process, Parametric models, Disasters
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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.
Journal of the Royal Statistical Society. Series C (Applied Statistics) © 1992 Royal Statistical Society