Bayesian Calibration of Inexact Computer Models

Matthew Plumlee

Journal of the American Statistical Association, 2017, vol. 112, issue 519, 1274-1285

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.

Date: 2017
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DOI: 10.1080/01621459.2016.1211016

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