Abstract Details
Activity Number:
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297
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 6, 2013 : 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 - #307718 |
Title:
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Sequential Monte Carlo with Adaptive Weights for Approximate Bayesian Computation
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Author(s):
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Fernando Bonassi*+ and Mike West
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Companies:
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Duke University and Duke University
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Keywords:
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Adaptive simulation ;
approximate Bayesian computation (ABC) ;
dynamic bionetwork models ;
importance sampling ;
mixture model emulators ;
sequential Monte Carlo (SMC)
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Abstract:
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Methods of Approximate Bayesian computation (ABC) are increasingly used for analysis of complex models. A major challenge for ABC is over-coming the often inherent problem of high rejection rates in the accept/reject methods based on prior:predictive sampling. A number of recent developments aim to address this with extensions based on sequential Monte Carlo (SMC) strategies. We build on this here, introducing an ABC SMC method that uses data-based adaptive weights. This easily implemented and computationally trivial extension of ABC SMC can very substantially improve acceptance rates, as is demonstrated in a series of examples with simulated and real data sets, including a currently topical example from dynamic modelling in systems biology applications.
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Authors who are presenting talks have a * after their name.
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