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Hierarchical Bayesian Analysis of Changepoint Problems
Author(s) -
Carlin Bradley P.,
Gelfand Alan E.,
Smith Adrian F. M.
Publication year - 1992
Publication title -
journal of the royal statistical society: series c (applied statistics)
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.205
H-Index - 72
eISSN - 1467-9876
pISSN - 0035-9254
DOI - 10.2307/2347570
Subject(s) - bayesian probability , statistics , computer science , bayesian hierarchical modeling , mathematics , bayes' theorem
SUMMARY 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.