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B0953
Title: Bayesian spatial homogeneity pursuit methods Authors:  Guanyu Hu - University of Missouri Columbia (United States) [presenting]
Abstract: The Bayesian spatial homogeneity pursuit methods will be discussed. To capture the spatial homogeneity, we develop a Markov random fields constraint mixture of finite mixture prior. An efficient Markov chain Monte Carlo (MCMC) algorithm is designed to estimate parameters and their uncertainty measures simultaneously. Extensive simulations are conducted to evaluate the empirical performance of the proposed models. Finally, we illustrate the performance of the model with different applications.