论文标题
分析随机计算机模型：与机会的评论
Analyzing Stochastic Computer Models: A Review with Opportunities
论文作者
论文摘要
在现代科学中，计算机模型通常用于理解复杂的现象，而繁荣的统计社区已经成长为分析它们。这篇综述旨在使随机计算机模型的越来越普遍的兴趣成为人们的关注 - 为从业者提供统计方法的目录，统计学家的入门观点（无论是熟悉确定性计算机模型还是熟悉确定性计算机模型），并强调与从业者和统计学家相关的开放性问题。高斯工艺替代模型在本综述中占据了中心地位，这些综述以及随机设置所需的几个扩展。讨论中突出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。带有数据和代码的启发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.
