The evolutionary forest algorithm | Zendy

Scotland Leman | Zendy; Marcy K. Uyenoyama | Zendy; Michael Lavine | Zendy; Yuguo Chen | Zendy

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The evolutionary forest algorithm

Author(s) -

Scotland Leman,

Marcy K. Uyenoyama,

Michael Lavine,

Yuguo Chen

Publication year - 2007

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btm264

Subject(s) - population , algorithm , tree (set theory) , context (archaeology) , posterior probability , computer science , monte carlo method , measure (data warehouse) , statistics , mathematics , data mining , biology , bayesian probability , combinatorics , paleontology , sociology , demography

Gene genealogies offer a powerful context for inferences about the evolutionary process based on presently segregating DNA variation. In many cases, it is the distribution of population parameters, marginalized over the effectively infinite-dimensional tree space, that is of interest. Our evolutionary forest (EF) algorithm uses Monte Carlo methods to generate posterior distributions of population parameters. A novel feature is the updating of parameter values based on a probability measure defined on an ensemble of histories (a forest of genealogies), rather than a single tree.

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