Learning genetic population structures using minimization of stochastic complexity

DOI10.3390/e12051102zbMath1229.92038OpenAlexW2019096419MaRDI QIDQ653380

Jukka Corander, Timo Koski, Mats Gyllenberg

Publication date: 9 January 2012

Published in: Entropy (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.3390/e12051102

zbMATH Keywords

population geneticsminimum description lengthfinite mixture modelsstatistical learningstructured populationfactorization of multivariate distributions

Mathematics Subject Classification ID

Applications of statistics to biology and medical sciences; meta analysis (62P10) Genetics and epigenetics (92D10) Systems biology, networks (92C42)

Uses Software

• STRUCTURE

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Bayesian unsupervised classification framework based on stochastic partitions of data and a parallel search strategy
• Bayesian analysis of population structure based on linked molecular information
• Bayesian predictive identification and cumulative classification of bacteria
• On the choice of a model to fit data from an exponential family
• Estimating the dimension of a model
• Jeffreys' prior is asymptotically least favorable under entropy risk
• Stochastic complexity in learning
• Classification of binary vectors by stochastic complexity.
• Bayesian network classifiers
• Bayesian predictiveness, exchangeability and sufficientness in bacterial taxonomy
• Random partition models and exchangeability for Bayesian identification of population struc\-ture
• Comments on approximating discrete probability distributions with dependence trees
• Probabilistic Networks and Expert Systems
• Minimum description length induction, Bayesianism, and Kolmogorov complexity
• A decision-theoretic extension of stochastic complexity and its applications to learning
• Fisher information and stochastic complexity
• Approximating discrete probability distributions with dependence trees