Identifying the active factors that have significant impacts on the output of the complex system is an important but challenging variable selection problem in computer experiments. In this paper, a Bayesian hierarchical Gaussian process model is developed and some latent indicator variables are embedded into this setting for the sake of labelling the important variables. The parameter estimation and variable selection can be processed simultaneously in a full Bayesian framework through an efficient Markov Chain Monte Carlo (MCMC) method -- Metropolis-within-Gibbs sampler. The much better performances of the proposed method compared with the related competitors are evaluated by the analysis of simulated examples and a practical application.

翻译：识别对复杂系统输出具有显著影响的活跃因子是计算机实验中一个重要且具有挑战性的变量选择问题。本文构建了一个贝叶斯层次高斯过程模型，并在该框架中嵌入若干潜指示变量以标记重要变量。通过高效的马尔可夫链蒙特卡洛（MCMC）方法——Gibbs抽样框架内的Metropolis算法，可在完全贝叶斯框架下同步实现参数估计与变量选择。通过对仿真算例和实际应用的分析评估，证明了所提方法相较于相关对比方法具有更优越的性能。