Dancik Garrett M, Dorman Karin S
Program in Bioinformatics & Computational Biology, Department of Statistics and Department of Genetics, Development & Cell Biology, Iowa State University, Ames, IA 50010, USA.
Bioinformatics. 2008 Sep 1;24(17):1966-7. doi: 10.1093/bioinformatics/btn329. Epub 2008 Jul 17.
Gaussian processes (GPs) are flexible statistical models commonly used for predicting output from complex computer codes. As such, GPs are well suited for the analysis of computer models of biological systems, which have been traditionally difficult to analyze due to their high-dimensional, non-linear and resource-intensive nature. We describe an R package, mlegp, that fits GPs to computer model outputs and performs sensitivity analysis to identify and characterize the effects of important model inputs.
高斯过程（GPs）是灵活的统计模型，常用于预测复杂计算机代码的输出。因此，高斯过程非常适合分析生物系统的计算机模型，由于其高维、非线性和资源密集型的性质，传统上这些模型很难进行分析。我们描述了一个R包mlegp，它将高斯过程应用于计算机模型输出，并进行敏感性分析，以识别和表征重要模型输入的影响。
