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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179
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Type:
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Contributed
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Date/Time:
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Monday, August 2, 2010 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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| Abstract - #308033 |
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Title:
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Nonstationary Gaussian Process Models via Treed Process-Convolutions
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Author(s):
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Waley Liang*+
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Companies:
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University of California, Santa Cruz
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Address:
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, , CA, ,
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Keywords:
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Nonstationary ;
Gaussian processes ;
Process-convolutions ;
Classification and Regression Trees ;
Bayesian ;
Markov Chain Monte Carlo
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Abstract:
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We introduce a new methodology in nonstationary modeling with Gaussian processes (GPs) that is geared towards large data sets. The GP in this model is formulated based on the convolution of a smoothing kernel with a white noise process. Nonstationarity in the GP is induced by partitioning the white noise process via Classification and Regression Trees (CART) and allowing the partitions to have separate variabilities. A Bayesian approach is used to estimate the model structure as well as the parameters. Performance of the model is illustrated on a large set of historical precipitation data.
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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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