Bayesian Computational Methods for Inference in Multiple Change-points Models
نویسندگان
چکیده
Multiple change-point models provide a exible and interpretable framework for representation of temporal heterogeneity in data. In addition to the locations of changepoints, these models typically involve parameters which specify the distributions of data between change-points and other quantities. However, the values of these parameters are usually unknown and need to be inferred from the data. We develop new Markov chain Monte Carlo algorithms which provide an e cient means for full Bayesian inference in the presence of parameter uncertainty. Performance is demonstrated on various examples.
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