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A Novel Bayesian Change-point Algorithm for Genome-wide Analysis of Diverse ChIPseq Data Types
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Fusion of Hidden Markov Random Field models and its Bayesian estimation
François Destrempes1, Jean-François Angers, Max Mignotte
1Département d'Informatique et de Recherche Opérationnelle, Université de Montréal, Pavilion André-Aisenstadt, Canada. destremp@iro.umontreal.ca
Abstract:
In this paper, we present a Hidden Markov Random Field (HMRF) data-fusion model. The proposed model is applied to the segmentation of natural images based on the fusion of colors and textons into Julesz ensembles. The corresponding Exploration/ Selection/Estimation (ESE) procedure for the estimation of the parameters is presented. This method achieves the estimation of the parameters of the Gaussian kernels, the mixture proportions, the region labels, the number of regions, and the Markov hyper-parameter. Meanwhile, we present a new proof of the asymptotic convergence of the ESE procedure, based on original finite time bounds for the rate of convergence.
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