Bayesian Treed Gaussian Process Models With an Application to Computer Modeling

• 成果类型: Article
• 署名作者: Gramacy, Robert B.; Lee, Herbert K. H.
• 署名单位: University of Cambridge; University of California System; University of California Santa Cruz
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214508000000689
• 发表日期: 2008
• 页码: 1119-1130
• 关键词: mixtures

摘要

Motivated by a computer experiment for the design of a rocket booster this article explores nonstationary modeling methodologies that couple stationary Gaussian processes with treed partioning is a simple but effective method for dealing with nonstationarity. The methodological developments and statistical computing details that make this approach efficient are described in detail. In addition to providing an analysis of the rocket booster, we show that our approach is effective in other areas as well.