Markov neighborhood estimation with linear complexity for random fields

Author

Talata, Zsolt

Author_Institution

Dept. of Math., Univ. of Kansas, Lawrence, KS, USA

fYear

2014

fDate

June 29 2014-July 4 2014

Firstpage

3042

Lastpage

3046

Abstract

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.

Keywords

Markov processes; computational complexity; set theory; Markov neighborhood estimation; Markov random fields; d-dimensional integer lattice; estimation error probability; finite state space; linear computation complexity; optimal likelihood ratio estimator; Computational complexity; Estimation error; Information theory; Lattices; Markov processes;

fLanguage

English

Publisher

ieee

Conference_Titel

Information Theory (ISIT), 2014 IEEE International Symposium on

Conference_Location

Honolulu, HI

Type

conf

DOI

10.1109/ISIT.2014.6875393

Filename

6875393

Link To Document

https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=1780605