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http://dx.doi.org/10.5351/CKSS.2006.13.3.621

Bayesian Multiple Change-point Estimation in Normal with EMC  

Kim, Jae-Hee (Department of Statistics, Duksung Women's University)
Cheon, Soo-Young (Department of Statistics, Texas A&M University, College Station)
Publication Information
Communications for Statistical Applications and Methods / v.13, no.3, 2006 , pp. 621-633 More about this Journal
Abstract
In this paper, we estimate multiple change-points when the data follow the normal distributions in the Bayesian way. Evolutionary Monte Carlo (EMC) algorithm is applied into general Bayesian model with variable-dimension parameters and shows its usefulness and efficiency as a promising tool especially for computational issues. The method is applied to the humidity data of Seoul and the final model is determined based on BIC.
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