Hidden markov random field model selection criteria based on mean field-like approximations

stanford, 1999, Fast Automatic Unsupervised Image Segmentation and Curve Detection in Spatial Point Processes

mclachlan, 1987, Mixture Models Inference and Applications to Clustering

hammersley, 1971, Markov Fields on Finite Graphs and Lattices.

10.1007/BFb0103042

10.1016/S0031-3203(02)00027-4

10.1109/34.865189

10.1098/rsta.1991.0132

chandler, 1987, Introduction to Modern Statistical Mechanics

besag, 1986, On the Statistical Analysis of Dirty Pictures, J Royal Statistical Soc B, 48, 259, 10.1111/j.2517-6161.1986.tb01412.x

stanford, 2001, Determining the Number of Colors or Gray Levels in an Image Using Approximate Bayes Factors: The Pseudolikelihood Information Criterion (PLIC), 10.21236/ADA459788

10.1016/0378-3758(95)00071-2

10.2307/2987782

10.1109/18.641558

10.1109/83.585235

peyrard, 2001, Approximations de Type Champ Moyen des Mod�les de Champ de Markov pour la Segmentation de Donn�es Spatiales

10.1109/TPAMI.1984.4767596

10.1109/34.134040

10.1109/ICASSP.1990.115992

10.1162/neco.1990.2.1.1

10.1007/978-94-011-5014-9_6

10.1109/34.566806

10.1016/S0378-3758(02)00318-X

10.1214/aoap/1034968138

gassiat, 2001, Likelihood Ratio Inequalities with Applications to Various Mixtures

raftery, 1995, Bayesian Model Selection in Social Research (with discussion), Sociological Methodology, 111, 10.2307/271063

10.2307/2289139

10.1093/comjnl/41.8.578

mclachlan, 2000, Finite Mixture Models, 10.1002/0471721182

newton, 1994, Approximate Bayesian Inference by the Weighted Likelihood Bootstrap (with discussion), J Royal Statistical Soc B, 56, 3, 10.1111/j.2517-6161.1994.tb01956.x

10.1080/01621459.1997.10474044

10.1214/aos/1176344136

10.2307/2291091

10.1214/aos/1176349027

rissanen, 1989, Stochastic Complexity in Statistical Inquiry, Word Scientific

akaike, 1973, Information Theory and an Extension of the Maximum Likelihood Principle, Proc Second Int'l Symp Information Theory, 267