Abstract #300554

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JSM 2002 Abstract #300554
Activity Number: 115
Type: Luncheons
Date/Time: Monday, August 12, 2002 : 12:30 PM to 1:50 PM
Sponsor: Section on Bayesian Stat. Sciences*
Abstract - #300554
Title: Non-parametric Bayesian Data Analysis
Author(s): Peter Muller*+
Affiliation(s): U. T. M. D. Anderson Cancer Center
Address: 1515 Holcombe Boulevard, Box 447, Houston, Texas, 77030-4009, USA
Keywords: kernel density estimation ; scatterplot smoothers
Abstract:

Non-parametric Bayesian Data Analysis traditionally refers to Bayesian models, which result in inference comparable to classical non-parametric inference, like kernel density estimation, scatterplot smoothers, etc. Such flexible inference is typically achieved by models with massively many parameters. We will discuss commonly used non-parametric Bayesian probability models and use in the context of traditional statistical problems.

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Revised March 2002