Abstract
Change point detection models aim to determine the most probable grouping for a given sample indexed on an ordered set. For this purpose, we propose a methodology based on exchangeable partition probability functions, specifically on Pitman’s sampling formula. Emphasis will be given to the Markovian case, in particular for discretely observed Ornstein-Uhlenbeck diffusion processes. Some properties of the resulting model are explained and posterior results are obtained via a novel Markov chain Monte Carlo algorithm.
Citation
Asael Fabian Martínez. Ramsés H. Mena. "On a Nonparametric Change Point Detection Model in Markovian Regimes." Bayesian Anal. 9 (4) 823 - 858, December 2014. https://doi.org/10.1214/14-BA878
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