Particle Markov Chain Monte Carlo for Multiple Change-point Problems
نویسندگان
چکیده
Multiple change-point models are a popular class of time series models which allow the description of temporal heterogeneity in data. We develop efficient Markov Chain Monte Carlo (MCMC) algorithms to perform Bayesian inference in this context. Our so-called Particle MCMC (PMCMC) algorithms rely on an efficient Sequential Monte Carlo (SMC) technique for change-point models, developed in [13], to build high-dimensional proposals. The construction of the new algorithms differs significantly from the PMCMC schemes proposed in [1]. We demonstrate the performance of our algorithms on various examples.
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