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A review on statistical inference methods for discrete Markov random fields

Stoehr, Julien (2017-04), A review on statistical inference methods for discrete Markov random fields. https://basepub.dauphine.fr/handle/123456789/19681

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Type
Document de travail / Working paper
External document link
https://arxiv.org/abs/1704.03331
Date
2017-04
Series title
Cahier de recherche CEREMADE, Université Paris-Dauphine
Published in
Paris
Pages
31
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Author(s)
Stoehr, Julien cc
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Abstract (EN)
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.
Subjects / Keywords
statistics; Markov random fields; parameter estimation; model selection