Learning Genetic Population Structures Using Minimization of Stochastic Complexity

• 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.

Ämnesord

NATURVETENSKAP  -- Matematik -- Sannolikhetsteori och statistik (hsv//swe)

NATURAL SCIENCES  -- Mathematics -- Probability Theory and Statistics (hsv//eng)

NATURVETENSKAP  -- Biologi -- Genetik (hsv//swe)

NATURAL SCIENCES  -- Biological Sciences -- Genetics (hsv//eng)

Nyckelord

factorization of multivariate distributions

finite mixture models

Minimum Description Length

population genetics

statistical learning

structured population

Mathematical statistics

Matematisk statistik

Genetics

Genetik

Publikations- och innehållstyp

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