Adaptive design of experiments based on gaussian processes

Evgeny Burnaev, Maxim Panov

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    36 Citations (Scopus)

    Abstract

    We consider a problem of adaptive design of experiments for Gaussian process regression. We introduce a Bayesian framework, which provides theoretical justification for some well-know heuristic criteria from the literature and also gives an opportunity to derive some new criteria. We also perform testing of methods in question on a big set of multidimensional functions.

    Original languageEnglish
    Title of host publicationStatistical Learning and Data Sciences - 3rd International Symposium, SLDS 2015, Proceedings
    EditorsAlexander Gammerman, Vladimir Vovk, Harris Papadopoulos
    PublisherSpringer Verlag
    Pages116-125
    Number of pages10
    Volume9047
    ISBN (Print)9783319170909
    DOIs
    Publication statusPublished - 2015
    Event3rd International Symposium on Statistical Learning and Data Sciences, SLDS 2015 - Egham, United Kingdom
    Duration: 20 Apr 201523 Apr 2015

    Publication series

    NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
    Volume9047
    ISSN (Print)0302-9743
    ISSN (Electronic)1611-3349

    Conference

    Conference3rd International Symposium on Statistical Learning and Data Sciences, SLDS 2015
    Country/TerritoryUnited Kingdom
    CityEgham
    Period20/04/1523/04/15

    Keywords

    • Active learning
    • Computer experiments
    • Gaussian processes
    • Sequential design

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