Bayesian Multiple Change-Point Estimation of Multivariate Mean Vectors for Small Data Cheon, Sooyoung; Yu, Wenxing;
A Bayesian multiple change-point model for small data is proposed for multivariate means and is an extension of the univariate case of Cheon and Yu (2012). The proposed model requires data from a multivariate noncentral -distribution and conjugate priors for the distributional parameters. We apply the Metropolis-Hastings-within-Gibbs Sampling algorithm to the proposed model to detecte multiple change-points. The performance of our proposed algorithm has been investigated on simulated and real dataset, Hanwoo fat content bivariate data.
Small data;change-point;noncentral t-distribution;Metropolis-Hastings-Within-Gibbs sampling;Hanwoo fat content;
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