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Activity Number:
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188
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Type:
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Topic Contributed
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Date/Time:
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Monday, July 30, 2007 : 2:00 PM to 3:50 PM
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Sponsor:
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IMS
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| Abstract - #308491 |
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Title:
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Coalescent-Based Inference of Population Dynamics with Gaussian Markov Random Field Temporal Smoothing
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Author(s):
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Vladimir N. Minin*+ and Marc A. Suchard
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Companies:
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University of California, Los Angeles and University of California, Los Angeles
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Address:
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Biomathematics, Los Angeles, CA, 90095-1766,
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Keywords:
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coalescent ; effective population size ; smoothing ; Gaussian Markov Random Field
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Abstract:
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Kingman's coalescent process opens the door for estimation of population genetics model parameters from molecular sequences. One paramount parameter of interest is the effective population size. Temporal variation of this quantity characterizes the demographic history of a population. We propose to exploit Gaussian Markov random fields (GMRFs) to achieve temporal smoothing of the effective population size in a Bayesian framework. In a simulation study, we demonstrate that the proposed temporal smoothing method successfully recovers "true" population size trajectories in all simulation scenarios. We apply our GMRF smoothing to sequences of hepatitis C virus contemporaneously sampled in Egypt and human influenza A hemagglutinin sequences serially sampled throughout three flu seasons.
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