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Abstract Details
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Activity Number:
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143
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Type:
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Invited
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Date/Time:
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Monday, August 1, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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International Society of Bayesian Analysis
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| Abstract - #300402 |
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Title:
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Bayesian Selection of Hidden Markov Random Fields
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Author(s):
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Jean-Michel Marin*+ and Lionel Cucala
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Companies:
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Universite Montpellier 2, France and Universite Montpellier 2, France
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Address:
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, , ,
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Keywords:
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mixture models ;
spatial dependences ;
Potts models ;
model choice ;
Chib's method ;
Laplace approximation
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Abstract:
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We introduce two techniques in order to select the number of components of a mixture model with spatial dependences. Typically, we consider an image field where the grey-scale values are Gaussian random variables depending on the component of the associated pixel and the components are distributed according to a Potts model. The first method comes from an approximation of the evidence based on the Chib's method. The second one is deduced from an approximation of the integrated completed likelihood using Laplace approximations. We compare these two techniques on real and simulated datasets.
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