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Activity Number:
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105
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Type:
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Topic Contributed
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Date/Time:
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Monday, August 3, 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 - #303534 |
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Title:
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Learning Bayesian Networks for Discrete Data
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Author(s):
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Faming Liang*+ and Jian Zhang
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Companies:
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Texas A&M University and The University of York
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Address:
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Department of Statistics, College Station, TX, 77843,
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Keywords:
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Model selection ; Bayesian network ; MCMC ; SAMC
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Abstract:
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Bayesian networks have received much attention in the recent literature. In this talk, we propose an approach to learn Bayesian networks using the stochastic approximation Monte Carlo (SAMC) algorithm. Our approach has two nice features. First, it possesses the self-adjusting mechanism and thus avoids essentially the local-trap problem suffered by conventional MCMC simulation-based approaches in learning Bayesian networks. Second, it falls into the class of dynamic importance sampling algorithms; the network features can be inferred by dynamically weighted averaging the samples generated in the learning process, and the resulting estimates can have much lower variation than the single model-based estimates. The numerical results indicate that our approach can mix much faster over the space of Bayesian networks than the conventional MCMC simulation-based approaches.
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