This is the program for the 2010 Joint Statistical Meetings in Vancouver, British Columbia.
Abstract Details
Activity Number:
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303
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Type:
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Contributed
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Date/Time:
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Tuesday, August 3, 2010 : 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 - #307253 |
Title:
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A Probability for Classification Based on the Mixture of Dirichlet Process Model
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Author(s):
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Ruth Fuentes-Garcia*+ and Ramses Mena and Stephen Graham Walker
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Companies:
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Universidad Nacional Autónoma de México and IIMAS-UNAM and University of Kent
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Address:
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, , 04510, MEXICO
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Keywords:
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Bayesian model ;
Mixture model ;
Gibbs sampler
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Abstract:
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An explicit probability distribution for classification purposes when observations are viewed on the real line and classifications are to be based on numerical orderings. The classification model is derived from a Bayesian nonparametric mixture of Dirichlet process model; with some modifications. The proposed probability model for classification relies on a numerical procedure based on a reversible Markov chain Monte Carlo (MCMC) algorithm for determining the probabilities.
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Authors who are presenting talks have a * after their name.
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