随机计算机模型分析：机遇与回顾
Analyzing Stochastic Computer Models: A Review with Opportunities
摘要：
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，一个蓬勃发展的统计社区已经发展起来。这篇评论的目的是引起人们对随机计算机模型日益流行的关注-为从业人员提供一个统计方法目录，为统计人员提供一个介绍性观点（无论是否熟悉确定性计算机模型），并强调与从业人员和统计人员有关的开放性问题。高斯过程代理模型在这篇评论的中心舞台，这些，以及随机设置所需的几个扩展，进行了解释。设计随机计算机实验和校准随机计算机模型的基本问题是突出的讨论。有指导意义的例子，数据和代码，用于描述的实施，并从各种方法的结果。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
