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Activity Number: 386
Type: Contributed
Date/Time: Wednesday, August 9, 2006 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #306660
Title: The Evolutionary Forest Algorithm
Author(s): Scotland Leman*+
Companies: Duke University
Address: ISDS, , 27707,
Keywords: speciation ; divergence ; MCMC ; Bayesian ; data augmentation
Abstract:

In this application, we address the demographic history of closely related members of the Drosophila pseudoobscura group. We estimate the joint posterior distribution of the time since speciation, backward migration rates and effective population sizes of the extant and ancestral populations. We base estimation on the multi-locus regions of DPS2002 and Rhodopsin 1 simultaneously linked by a unique speciation event. Summary statistics, rather than entire nucleotide sequences, permits a compact description of the genealogy of the sample. Consideration of an augmented random variable, comprised of multiple genealogies or forest, dramatically increases sampling efficiency. This method of using a forest of genealogical histories converges marginally to the posterior distributions of interest and convergence is improved on compared to methods in which a single history is considered.


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