Bayesian and Monte Carlo change-point detection
نویسندگان
چکیده
The contribution presents to analyses and comparison of the recursive (sliding window) Bayesian autoregressive normalized change-point detector (RBACDN) and the reversible jump Markov chain Monte Carlo method (RJMCMC) when they are used for the localization of signal changes (change-point detection). The choice of priors and parameter setting for the RJMCMC and the RBACDN are discussed. The evaluation of both the algorithms performance is shown, and their accuracy and some illustrative examples with synthetic and real signals are presented. Key-Words: Monte Carlo method, Bayesian change-point detector, multiple changes, signal segmentation
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