learning genetic population structures using minimization of stochastic complexity

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ID: 214077
2010
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Abstract
Considerable research efforts have been devoted to probabilistic modeling of genetic population structures within the past decade. In particular, a wide spectrum of Bayesian models have been proposed for unlinked molecular marker data from diploid organisms. Here we derive a theoretical framework for learning genetic population structure of a haploid organism from bi-allelic markers for which potential patterns of dependence are a priori unknown and to be explicitly incorporated in the model. Our framework is based on the principle of minimizing stochastic complexity of an unsupervised classification under tree augmented factorization of the predictive data distribution. We discuss a fast implementation of the learning framework using deterministic algorithms.
Reference Key
corander2010entropylearning Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors ;Jukka Corander;Mats Gyllenberg;Timo Koski
Journal European journal of medicinal chemistry
Year 2010
DOI
10.3390/e12051102
URL
http://www.mdpi.com/1099-4300/12/5/1102/
Keywords
population genetics statistical learning

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