A review on statistical inference methods for discrete Markov random fields

Julien Stoehr (1) , Richard Everitt (2) , Matthew T. Moores (3)

1 CEREMADE - CEntre de REcherches en MAthématiques de la DEcision
2 Department of Statistics [Warwick]
3 University of Wollongong [Australia]

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.

Mots clés: Parameter Inference; Model selection; Markov random field

Domaines: Méthodologie [stat.ME]

Julien Stoehr, Richard Everitt, Matthew T. Moores. A review on statistical inference methods for discrete Markov random fields. 2017. ⟨hal-01462078v2⟩