Abstract Details
Activity Number:
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278
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 5, 2014 : 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 #311166
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View Presentation
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Title:
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On the Prior and Posterior Distributions Used in Graphical Modeling
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Author(s):
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Marco Scutari*+
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Companies:
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University College London
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Keywords:
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Markov networks ;
Bayesian networks ;
random graphs ;
structure learning ;
multivariate discrete distributions
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Abstract:
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Graphical model learning and inference are often performed using Bayesian techniques. In particular, learning is usually performed in two separate steps. First, the graph structure is learned from the data; then the parameters of the model are estimated conditional on that graph structure. While the probability distributions involved in this second step have been studied in depth, those used in the first step have not been explored in as much detail. If we look at them as a function of the possible edges of the graph, their properties define measures of structural variability for both Bayesian and Markov networks. Such measures have the advantage of an intuitive geometric interpretation, and can be used to improve the estimation of optimal values for tuning parameters and for model validation. Furthermore, they provide useful guidelines in defining new priors with desirable properties.
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Authors who are presenting talks have a * after their name.
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