Default priors for Gaussian processes

RMB Paulo

Research output: Contribution to journalArticle (Academic Journal)peer-review

62 Citations (Scopus)

Abstract

Motivated by the statistical evaluation of complex computer models, we deal with the issue of objective prior specification for the parameters of Gaussian processes. In particular, we derive the Jeffreys-rule, independence Jeffreys and reference priors for this situation, and prove that the resulting posterior distributions are proper under a quite general set of conditions. A proper flat prior strategy, based on maximum likelihood estimates, is also considered, and all priors are then compared on the grounds of the frequentist properties of the ensuing Bayesian procedures. Computational issues are also addressed in the paper, and we illustrate the proposed solutions by means of an example taken from the field of complex computer model validation.
Translated title of the contributionDefault priors for Gaussian processes
Original languageEnglish
Pages (from-to)556 - 582
Number of pages27
JournalAnnals of Statistics
Volume33 (2)
DOIs
Publication statusPublished - Apr 2005

Bibliographical note

Publisher: Institute of Mathematical Statistics
Other identifier: IDS Number: 936KM

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