On Identifying the Optimal Number of Population Clusters via the Deviance Information Criterion | Zendy

Hong Gao | Zendy; Katarzyna Bryc | Zendy; Carlos D. Bustamante | Zendy

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On Identifying the Optimal Number of Population Clusters via the Deviance Information Criterion

Author(s) -

Hong Gao,

Katarzyna Bryc,

Carlos D. Bustamante

Publication year - 2011

Publication title -

plos one

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.99

H-Index - 332

ISSN - 1932-6203

DOI - 10.1371/journal.pone.0021014

Subject(s) - deviance information criterion , bayesian information criterion , deviance (statistics) , cluster analysis , coalescent theory , bayesian probability , computer science , population , model selection , a priori and a posteriori , data mining , selection (genetic algorithm) , statistics , bayesian inference , econometrics , machine learning , artificial intelligence , mathematics , biology , phylogenetic tree , medicine , philosophy , environmental health , epistemology , gene , biochemistry

Inferring population structure using Bayesian clustering programs often requires a priori specification of the number of subpopulations,, from which the sample has been drawn. Here, we explore the utility of a common Bayesian model selection criterion, the Deviance Information Criterion (DIC), for estimating. We evaluate the accuracy of DIC, as well as other popular approaches, on datasets generated by coalescent simulations under various demographic scenarios. We find that DIC outperforms competing methods in many genetic contexts, validating its application in assessing population structure.

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