Markov neighborhood estimation with linear complexity for random fields

Markov random fields on the d-dimensional integer lattice with finite state space are considered, and the problem of estimation of the basic neighborhood from a single realization observed in a finite region is addressed. The Optimal Likelihood Ratio (OLR) estimator is introduced. Its nearly linear computation complexity is shown, and a bound on the probability of the estimation error is proved that implies strong consistency.