JSM 2005 Online Program

Abstract #303560

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	256
Type:	Topic Contributed
Date/Time:	Tuesday, August 9, 2005 : 10:30 AM to 12:20 PM
Sponsor:	Section on Statistical Computing
Abstract - #303560
Title:	Adaptive Exploration of Computer Experiment Parameter Spaces
Author(s):	Robert B. Gramacy*+ and Herbert Lee and William G. Macready
Companies:	University of California, Santa Cruz and University of California, Santa Cruz and NASA Ames Research Center
Address:	Baskin School of Engineering, Santa Cruz, CA, 95064,
Keywords:	design of computer exeriments ; active learning ; Gaussian process ; treed partitioning ; Bayesian hierarchical model ; parallel mcmc
Abstract:	Many complex phenomena are difficult to investigate directly through controlled experiments. Instead, computer simulation is becoming a commonplace alternative to providing insight into such phenomena. The drive toward higher fidelity simulation continues to tax the fastest of computers, even in highly distributed computing environments. Computational fluid dynamics simulations, in which fluid flow phenomena are modeled, are an excellent example---fluid flows over complex surfaces may be modeled accurately but only at the cost of supercomputer resources. In this paper, we discuss the problem of fitting a response surface for a computer model when we also have the ability to design the experiment adaptively, updating the experiment as we learn about the model---a task to which we feel the Bayesian approach is particularly well-suited.

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