Ch. Wu et Pc. Doerschuk, TREE APPROXIMATIONS TO MARKOV RANDOM-FIELDS, IEEE transactions on pattern analysis and machine intelligence, 17(4), 1995, pp. 391-402
Methods for approximately computing the marginal probability mass func
tions and means of a Markov random field (MRF) by approximating the la
ttice by a tree are described. Applied to the a posteriori MRP these m
ethods solve Bayesian spatial pattern classification and image restora
tion problems. The methods are described, several theoretical results
concerning fixed-point problems are proven, and four numerical example
s are presented, including comparison with optimal estimators and the
Iterated Conditional Mode estimator and including two agricultural opt
ical remote sensing problems.