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Activity Number:
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201
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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 : 2:00 PM to 3:50 PM
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Sponsor:
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IMS
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| Abstract - #303389 |
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Title:
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Conditional Independence Models via Filtrations
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Author(s):
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Simon Lunagomez*+ and Sayan Mukherjee and Robert L. Wolpert
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Companies:
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Duke University and Duke University and Duke University
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Address:
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, , 27708,
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Keywords:
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Graphical models ; Computational topology ; Geoemtric random graphs ; Conditional independence
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Abstract:
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We formulate a novel approach to infer conditional independence models or Markov structure of a multivariate distribution. Specifically, our objective is to place informative prior distributions over decomposable graphs and sample efficiently from the induced posterior distribution. The key idea we develop in this paper is a parameterization of decomposable hypergraphs using the geometry of points in m-dimensional space. This allows for specification of informative priors on decomposable graphs by priors on a finite set of points. The constructions we use have been well studied in the fields of computational topology and random geometric graphs. We develop the framework underlying this idea and illustrate its efficacy using simulations.
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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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