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Abstract Details

Activity Number: 179
Type: Contributed
Date/Time: Monday, August 2, 2010 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract - #308033
Title: Nonstationary Gaussian Process Models via Treed Process-Convolutions
Author(s): Waley Liang*+
Companies: University of California, Santa Cruz
Address: , , CA, ,
Keywords: Nonstationary ; Gaussian processes ; Process-convolutions ; Classification and Regression Trees ; Bayesian ; Markov Chain Monte Carlo

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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