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Hierarchical Bayesian Analysis of Changepoint Problems

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  • Bradley P. Carlin
  • Alan E. Gelfand
  • Adrian F. M. Smith

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.

Suggested Citation

  • Bradley P. Carlin & Alan E. Gelfand & Adrian F. M. Smith, 1992. "Hierarchical Bayesian Analysis of Changepoint Problems," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 41(2), pages 389-405, June.
    • Handle: RePEc:bla:jorssc:v:41:y:1992:i:2:p:389-405
    DOI: 10.2307/2347570
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