Analyzing Stochastic Computer Models: A Review with Opportunities
Abstract
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.
Cite
@article{arxiv.2002.01321,
title = {Analyzing Stochastic Computer Models: A Review with Opportunities},
author = {Evan Baker and Pierre Barbillon and Arindam Fadikar and Robert B. Gramacy and Radu Herbei and David Higdon and Jiangeng Huang and Leah R. Johnson and Pulong Ma and Anirban Mondal and Bianica Pires and Jerome Sacks and Vadim Sokolov},
journal= {arXiv preprint arXiv:2002.01321},
year = {2020}
}
Comments
48 pages, 8 figures