Analyzing Stochastic Computer Models: A Review with Opportunities
Analyzing Stochastic Computer Models: A Review with Opportunities
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DOI：
10.1214/21-sts822
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发表时间：
2020-02
影响因子：
5.7
通讯作者：
Evan Baker;P. Barbillon;A. Fadikar;R. Gramacy;Radu Herbei;D. Higdon;Jiangeng Huang;L. Johnson
中科院分区：
文献类型：
--
作者：
Evan Baker;P. Barbillon;A. Fadikar;R. Gramacy;Radu Herbei;D. Higdon;Jiangeng Huang;L. Johnson
在现代科学中，计算机模型经常被用来理解复杂的现象，并且围绕分析它们而发展出一个蓬勃发展的统计社区。本综述旨在引起人们对日益流行的随机计算机模型的关注——为从业者提供统计方法目录，为统计学家（无论是否熟悉确定性计算机模型）提供介绍性观点，并强调与从业者和统计学家相关的开放性问题。高斯过程代理模型在本次审查中占据中心地位，并且对这些模型以及随机设置所需的几个扩展进行了解释。讨论中重点讨论了设计随机计算机实验和校准随机计算机模型的基本问题。带有数据和代码的指导性示例用于描述各种方法的实现和结果。
In modern science, computer models are often used to understand complex phenomena, and a thriving statistical community has grown around analyzing them. This review aims to bring a spotlight to the growing prevalence of stochastic computer models -- providing a catalogue of statistical methods for practitioners, an introductory view for statisticians (whether familiar with deterministic computer models or not), and an emphasis on open questions of relevance to practitioners and statisticians. Gaussian process surrogate models take center stage in this review, and these, along with several extensions needed for stochastic settings, are explained. The basic issues of designing a stochastic computer experiment and calibrating a stochastic computer model are prominent in the discussion. Instructive examples, with data and code, are used to describe the implementation of, and results from, various methods.