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- W2019979120 abstract "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 <TEX>$t$</TEX>-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." @default.
- W2019979120 created "2016-06-24" @default.
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- W2019979120 date "2012-12-31" @default.
- W2019979120 modified "2023-09-26" @default.
- W2019979120 title "Bayesian Multiple Change-Point Estimation of Multivariate Mean Vectors for Small Data" @default.
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