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Activity Number:
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414
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Type:
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Topic Contributed
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Date/Time:
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Wednesday, August 5, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #303624 |
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Title:
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Sequential Monte Carlo in Model Comparison: Example in Cellular Dynamics in Systems Biology
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Author(s):
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Chiranjit Mukherjee*+ and Y. Tanouchi and L. You and Mike West
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Companies:
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Duke University and Duke University and Duke University and Duke University
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Address:
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, , ,
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Keywords:
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Distributed Computing ; Dynamic Network Models ; Marginal Likelihood ; Model Comparison ; Particle Filtering ; Systems Biology
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Abstract:
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Sequential Monte Carlo analysis of time series provides a direct approach to evaluating approximate model marginal likelihoods for model comparison. We exemplify this in studies of dynamic bacterial communication in systems biology, where a long sequence of state vectors follow a complicated nonlinear dynamic model with several de fining biochemical parameters. MCMC methods do not mix well in these contexts, and do not lead easily to reliable estimates of model marginal likelihood. We develop an auxiliary particle filtering algorithm that simultaneously updates latent states and fi xed parameters. Our algorithm takes advantage of distributed computing to carry forward a huge number of particles to ensure accuracy of the estimates. Marginal likelihood computation is developed and illustrated in evaluation of relevance of selected model components.
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