Bayesian Analysis of a Two-State Markov Modulated Poisson Process |
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Authors: | Steven L. Scott |
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Affiliation: | Marshall School of Business, Bridge Hall 401-H, University of Southern California , Los Angeles , CA , 90089-1421 , USA |
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Abstract: | Abstract We postulate observations from a Poisson process whose rate parameter modulates between two values determined by an unobserved Markov chain. The theory switches from continuous to discrete time by considering the intervals between observations as a sequence of dependent random variables. A result from hidden Markov models allows us to sample from the posterior distribution of the model parameters given the observed event times using a Gibbs sampler with only two steps per iteration. |
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Keywords: | Forward-backward Gibbs sampler Hidden Markov model Markov chain Monte Carlo |
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