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Abstract Details
Activity Number:
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661
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Type:
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Contributed
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Date/Time:
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Thursday, August 4, 2011 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Statistical Computing
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Abstract - #301741 |
Title:
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A Fully Adaptive Interacting Wang-Landau Algorithm for Automatic Density Exploration
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Author(s):
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Luke Bornn*+
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Companies:
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University of British Columbia
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Address:
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, , BC, V6T 1Z2, Canada
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Keywords:
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Monte Carlo ;
Wang-Landau ;
Multicanonical sampling ;
Adaptive Monte Carlo ;
Particle Methods ;
Parallel
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Abstract:
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In this paper, the authors propose an adaptive density exploration algorithm combining and expanding upon components from various adaptive Markov chain Monte Carlo algorithms. With the Wang-Landau algorithm at its heart, the proposed method adopts several adaptive strategies for improved performance. Firstly, the algorithm is run on interacting parallel chains -- a feature which both decreases computational cost as well as stabilizes the algorithm's flat histogram mechanism. Through interaction, the chains are better able to bias the density to improve exploration. Secondly, a novel adaptive binning strategy is employed, removing the primary obstacle encountered with the Wang-Landau algorithm, namely that of specifying the bins through which to partition the state-space. Thirdly, a standard adaptive proposal is employed to improve the algorithm's ability to explore the already flattened density. The algorithm's performance is studied in several examples.
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