Learning genetic population structures using minimization of stochastic complexity
type: Item
instance of: scholarly article
title: Learning genetic population structures using minimization of stochastic complexity
zbMATH Open document ID: 1229.92038
author: Jukka Corander
author: Mats Gyllenberg
author: Timo Koski
published in: Entropy
publication date: 2012-01-09T00:00:00Z
review text: Summary: 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.
Mathematics Subject Classification ID: 92C42
Mathematics Subject Classification ID: 92D10
Mathematics Subject Classification ID: 62P10
zbMATH DE Number: 5995874
zbMATH Keywords: factorization of multivariate distributions
zbMATH Keywords: finite mixture models
zbMATH Keywords: minimum description length
zbMATH Keywords: population genetics
zbMATH Keywords: statistical learning
zbMATH Keywords: structured population
describes a project that uses: STRUCTURE
MaRDI profile type: Publication
full work available at URL: https://doi.org/10.3390/e12051102
OpenAlex ID: W2019096419
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DOI: 10.3390/E12051102
description: scientific article; zbMATH DE number 5995874