This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
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Activity Number:
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401
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Type:
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Topic Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
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Sponsor:
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IMS
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| Abstract - #308426 |
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Title:
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Inferential Methods for Graphical Models
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Author(s):
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Saeid Yasamin*+
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Companies:
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Stanford University
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Address:
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Department of Statistics - Sequoia Hall, Stanford, CA, 94305,
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Keywords:
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Graphical models, Gaussian Bayesian networks, DAGs
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Abstract:
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Graphical models are useful for describing conditional independences present in high dimensional probability distributions. Hence they have become standard tools in high dimensional inference. We present a novel approach for model selection and estimation for a general class of graphical models. We compare the method to other methods in the literature and study its theoretical properties. The new method is applied to both simulated and real datasets to illustrate its performance and applicability.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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