Bayesian inference with rescaled Gaussian process priors

A.W. Vaart, van der, J.H. Zanten, van

    Research output: Contribution to journalArticleAcademicpeer-review

    42 Citations (Scopus)
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    We use rescaled Gaussian processes as prior models for functional parameters in nonparametric statistical models. We show how the rate of contraction of the posterior distributions depends on the scaling factor. In particular, we exhibit rescaled Gaussian process priors yielding posteriors that contract around the true parameter at optimal convergence rates. To derive our results we establish bounds on small deviation probabilities for smooth stationary Gaussian processes.
    Original languageEnglish
    Pages (from-to)433-448
    JournalElectronic Journal of Statistics
    Publication statusPublished - 2007


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