A consistent model selection procedure for Markov random fields based on penalized pseudolikelihood

DOI10.1214/aoap/1034968138zbMath0856.62082OpenAlexW2110232401MaRDI QIDQ1814745

Chuanshu Ji, Lynne Seymour

Publication date: 12 February 1997

Published in: The Annals of Applied Probability (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1214/aoap/1034968138

zbMATH Keywords

model selectionphase transitionssimulation resultsimage analysistexture synthesismean-square errorMarkov random fieldsGibbs random fieldsmoderate deviation probabilitiesmaximum pseudolikelihood estimatorpenalized pseudo-likelihood

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Random fields; image analysis (62M40) Computing methodologies for image processing (68U10)

Related Items (10)

Spatial clustering of time series via mixture of autoregressions models and Markov random fields ⋮ Region selection in Markov random fields: Gaussian case ⋮ Structure recovery for partially observed discrete Markov random fields on graphs under not necessarily positive distributions ⋮ Posterior convergence and model estimation in Bayesian change-point problems ⋮ Goodness of fit tests for a class of Markov random field models ⋮ Selection of the neighborhood structure for space-time Markov random field models ⋮ Neighborhood radius estimation for variable-neighborhood random fields ⋮ Consistent estimation of the basic neighborhood of Markov random fields ⋮ Modelling persistence diagrams with planar point processes, and revealing topology with bagplots ⋮ Unnamed Item

Uses Software

• spatial

Cites Work

• A Bayesian analysis of the minimum AIC procedure
• On consistency of a class of estimators for exponential families of Markov random fields on the lattice
• Gibbs measures and phase transitions
• Estimating the dimension of a model
• On model selection and the arc sine laws
• Hidden Markov random fields
• Approximate Bayes model selection procedures for Gibbs-Markov random fields
• Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
• Estimation and choice of neighbors in spatial-interaction models of images
• A new look at the statistical model identification
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