Year: 2022
Type: article
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.
Source: Statistical Science
DOI: 10.1214/21-sts822
Institutions: University of Exeter, AgroParisTech, Université Paris-Saclay, Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Mathématiques et Informatique Appliquées, Argonne National Laboratory, Virginia Tech, The Ohio State University, Genentech, Statistical and Applied Mathematical Sciences Institute, Duke University, Case Western Reserve University, Mitre (United States), National Institute of Statistical Sciences, George Mason University
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3082154183