Implied distributions in multiple change point problems

Authors:
J. A. Aston;J. Y. Peng;D. E. Martin
Affiliations:
Centre for Research in Statistical Methodology, University of Warwick, Coventry, UK;Institute of Information Science, Academia Sinica, Taipei, Taiwan and Institute of Biomedical Informatics, National Yang-Ming University, Taipei, Taiwan;Dept. of Statistics, North Carolina State University, Raleigh, USA
Venue:
Statistics and Computing
Year:
2012

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Abstract

A method for efficiently calculating exact marginal, conditional and joint distributions for change points defined by general finite state Hidden Markov Models is proposed. The distributions are not subject to any approximation or sampling error once parameters of the model have been estimated. It is shown that, in contrast to sampling methods, very little computation is needed. The method provides probabilities associated with change points within an interval, as well as at specific points.