MCMC for hidden continuous - time
نویسندگان
چکیده
Hidden Markov models have proved to be a very exible class of models, with many and diverse applications. Recently Markov chain Monte Carlo (MCMC) techniques have provided powerful computational tools to make inferences about the parameters of hidden Markov models, and about the unobserved Markov chain, when the chain is deened in discrete time. We present a general algorithm, based on reversible jump MCMC, for inference in hidden Markov models where the unobserved chain runs in continuous time. The method is illustrated using two examples. One is a relatively simple application to Markov modulated Poisson processes. The second is a more complex problem of inference from single ion channel data, and serves to demonstrate the power and exibility of the algorithm.
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