Analyzing Stochastic Computer Models: A Review with Opportunities - AgroParisTech Access content directly
Journal Articles Statistical Science Year : 2022

Analyzing Stochastic Computer Models: A Review with Opportunities


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.
Fichier principal
Vignette du fichier
2002.01321.pdf (1.58 Mo) Télécharger le fichier
Origin Files produced by the author(s)

Dates and versions

hal-03566688 , version 1 (11-04-2022)



Evan Baker, Pierre M Barbillon, Arindam Fadikar, Robert Gramacy, Radu Herbei, et al.. Analyzing Stochastic Computer Models: A Review with Opportunities. Statistical Science, 2022, 37 (1), pp.64-89. ⟨10.1214/21-STS822⟩. ⟨hal-03566688⟩
128 View
201 Download



Gmail Mastodon Facebook X LinkedIn More