Learning Genetic Population Structures Using Minimization of Stochastic Complexity

• Main Authors: Jukka Corander, Mats Gyllenberg, Timo Koski
• Format: Article
• Language: English
• Published: MDPI AG 2010-05-01
• Series: Entropy
• Subjects: factorization of multivariate distributions; finite mixture models; Minimum Description Length; population genetics; statistical learning; structured population
• Online Access: http://www.mdpi.com/1099-4300/12/5/1102/
• ISSN: 1099-4300

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.