Hidden Markov random fields and the genetic structure of the scandinavian brown bear population
Journal de la société française de statistique, Tome 148 (2007) no. 1, pp. 31-38.

Les algorithmes de classification bayésienne spatiale sont utiles afin d'étudier la structure génétique de populations pour lesquelles on observe une variation des fréquences d'allèles généralement continue en espace, mais localement interrompue par de petites discontinuités. Dans cet article, nous présentons une synthèse de travaux récents appliquant ces algorithmes à l'étude de l'ours brun de Scandinavie et nous résumons les connaissances actuelles sur la structure de cette population potentiellement utiles pour sa conservation.

Spatial bayesian clustering algorithms can provide correct inference of population genetic structure when applied to populations for which continuous variation of allele frequencies is disrupted by small discontinuities. Here we review works which used bayesian clustering algorithms for studying the Scandinavian brown bears, with particular attention to a recent method based on hidden Markov random field. We provide a summary of current knowledge about the genetic structure of this endangered population potentially useful for its conservation.

Mots clés : structure génétique des populations, analyse bayésienne spatiale, analyse par méthodes d'agrégation, ours brun de Scandinavie
     author = {Ancelet, Sophie and Guillot, Gilles and Fran\c cois, Olivier},
     title = {Hidden Markov random fields and the genetic structure of the scandinavian brown bear population},
     journal = {Journal de la soci\'et\'e fran\c caise de statistique},
     pages = {31--38},
     publisher = {Soci\'et\'e fran\c caise de statistique},
     volume = {148},
     number = {1},
     year = {2007},
     language = {en},
     url = {archive.numdam.org/item/JSFS_2007__148_1_31_0/}
Ancelet, Sophie; Guillot, Gilles; François, Olivier. Hidden Markov random fields and the genetic structure of the scandinavian brown bear population. Journal de la société française de statistique, Tome 148 (2007) no. 1, pp. 31-38. http://archive.numdam.org/item/JSFS_2007__148_1_31_0/

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