Nonparametric Empirical Bayes for the Dirichlet Process Mixture Model

Topics:
Electrical and Electronic
Tags:
Business Operations,
Parameter,
Research & Development,
University Of California At Berkeley
Source:
University of California, Berkeley

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Overview: The Dirichlet process prior allows flexible nonparametric mixture modeling. The number of mixture components is not specified in advance and can grow as new data come in. However, the behavior of the model is sensitive to the choice of the parameters, including an infinite-dimensional distributional parameter G0. Most previous applications have either fixed G0 as a member of a parametric family or treated G0 in a Bayesian fashion, using parametric prior specifications. In contrast, they have developed an adaptive nonparametric method for constructing smooth estimates of G0.

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Format: PDF | Size: 1,741KB | Date: Oct 2004 | Pages: 12

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