Bayesian inference;
Genetic population structure;
Statistical learning theory;
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摘要:
We introduce a Bayesian theoretical formulation of the statistical learning problem concerning the genetic structure of populations. The two key concepts in our derivation are exchangeability in its various forms and random allocation models. Implications of our results to empirical investigation of the population structure are discussed.
机构:
Univ Texas Austin, Dept Stat & Data Sci, Austin, TX 78712 USAUniv Texas Austin, Dept Stat & Data Sci, Austin, TX 78712 USA
Zanini, Carlos Tadeu Pagani
Mueller, Peter
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机构:
Univ Texas Austin, Dept Stat & Data Sci, Austin, TX 78712 USAUniv Texas Austin, Dept Stat & Data Sci, Austin, TX 78712 USA
Mueller, Peter
Ji, Yuan
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机构:
Univ Chicago, NorthShore Univ HealthSyst, Computat Genom & Med, Chicago, IL 60637 USA
Univ Chicago, Dept Publ Hlth Sci, Chicago, IL 60637 USAUniv Texas Austin, Dept Stat & Data Sci, Austin, TX 78712 USA
Ji, Yuan
Quintana, Fernando A.
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机构:
Pontificia Univ Catolica Chile, Dept Estadist, Santiago, ChileUniv Texas Austin, Dept Stat & Data Sci, Austin, TX 78712 USA