Bayesian Calibration of Inexact Computer Models

Matthew Plumlee*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

36 Scopus citations

Abstract

Bayesian calibration is used to study computer models in the presence of both a calibration parameter and model bias. The parameter in the predominant methodology is left undefined. This results in an issue, where the posterior of the parameter is suboptimally broad. There has been no generally accepted alternatives to date. This article proposes using Bayesian calibration, where the prior distribution on the bias is orthogonal to the gradient of the computer model. Problems associated with Bayesian calibration are shown to be mitigated through analytic results in addition to examples. Supplementary materials for this article are available online.

Original languageEnglish (US)
Pages (from-to)1274-1285
Number of pages12
JournalJournal of the American Statistical Association
Volume112
Issue number519
DOIs
StatePublished - Jul 3 2017

Keywords

  • Calibration
  • Computer experiments
  • Deterministic models
  • Gaussian processes
  • Identifiability
  • Kriging
  • Model inadequacy
  • Orthogonal processes
  • Uncertainty quantification

ASJC Scopus subject areas

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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