Approximate Bayes Model Selection Procedures for Markov Random Fields
نویسندگان
چکیده
For applications in texture synthesis, we derive two approximate Bayes criteria for selecting a model from a collection of Markov random fields. The first criterion is based on a penalized maximum likelihood. The second criterion, a Markov chain Monte Carlo approximation to the first, has distinct computational advantages. Some simulation results are also presented.
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تاریخ انتشار 2008