Bayesian Indicator Variable Selection in Gaussian Process for Computer Experiments

Abstract

In the past three decades the analysis of computer experiments has received a lot of attention and plays a more and more important role in solving different scientific and engineering problems In this thesis we are interested in the variable selection problems for Gaussian process model In computer experiment here we not only focus on the mean function but also take covariance structure into account To accomplish our goal indicators are added into the model to denote if the variables are active or not Two Bayesian variable selection algorithms are proposed In addition to the simulation studies several real examples are also used to illustrate the of the proposed methods

Date of Award	2018 Jun 26
Original language	English
Supervisor	Ray-Bing Chen (Supervisor)