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A consistent model selection procedure for Markov random fields based on penalized pseudolikelihood. (English) Zbl 0856.62082
Summary: Motivated by applications in texture synthesis, we propose a model selection procedure for Markov random fields based on penalized pseudo-likelihood. The procedure is shown to be consistent for choosing the true model, even for Gibbs random fields with phase transitions. As a by-product, rates for the restricted mean-square error and moderate deviation probabilities are derived for the maximum pseudolikelihood estimator. Some simulation results are presented for the selection procedure.

MSC:
62M40 Random fields; image analysis
62F12 Asymptotic properties of parametric estimators
68U10 Computing methodologies for image processing
Software:
spatial
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