A review on statistical inference methods for discrete Markov random fields

2017/04/11 by Stoehr, Julien · 1 citation
#FOS: Computer and information sciences #Methodology (stat.ME)

paper · doi:10.48550/arxiv.1704.03331

Abstract

Developing satisfactory methodology for the analysis of Markov random field is a very challenging task. Indeed, due to the Markovian dependence structure, the normalizing constant of the fields cannot be computed using standard analytical or numerical methods. This forms a central issue for any statistical approach as the likelihood is an integral part of the procedure. Furthermore, such unobserved fields cannot be integrated out and the likelihood evaluation becomes a doubly intractable problem. This report gives an overview of some of the methods used in the literature to analyse such observed or unobserved random fields.

Cited by

• Markov Random Fields: Structural Properties, Phase Transition, and Response Function Analysis

Related

• Approximations and bounds for binary Markov random fields
• Computing the Cramer-Rao bound of Markov random field parameters: Application to the Ising and the Potts models
• MLE-induced Likelihood for Markov Random Fields
• A novel approach for Markov Random Field with intractable normalising constant on large lattices
• Simultaneous Inference for Non-Stationary Random Fields, with Application to Gridded Data Analysis