Multiple change-point detection of multivariate mean vectors with the Bayesian approach

Bayesian multiple change-point models are proposed for multivariate means. The models require that the data be from a multivariate normal distribution with a truncated Poisson prior for the number of change-points and conjugate priors for the distributional parameters. We apply the stochastic approximation Monte Carlo (SAMC) algorithm to the multiple change-point detection problems. Numerical results show that SAMC makes a significant improvement over RJMCMC for complex Bayesian model selection problems in change-point estimation.

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