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Adaptive Design of Experiments Based on Gaussian Processes

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Statistical Learning and Data Sciences (SLDS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9047))

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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.

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Author information

Authors and Affiliations

  1. Institute for Information Transmission Problems, Bolshoy Karetny per. 19, Moscow, 127994, Russia

    Evgeny Burnaev & Maxim Panov

  2. DATADVANCE, LLC, Pokrovsky blvd. 3, Moscow, 109028, Russia

    Evgeny Burnaev & Maxim Panov

  3. PreMoLab, MIPT, Institutsky per. 9, Dolgoprudny, 141700, Russia

    Evgeny Burnaev & Maxim Panov

Authors
  1. Evgeny Burnaev
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  2. Maxim Panov
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Corresponding author

Correspondence to Maxim Panov .

Editor information

Editors and Affiliations

  1. University of London, Egham, Surrey, United Kingdom

    Alexander Gammerman

  2. University of London, Egham, Surrey, United Kingdom

    Vladimir Vovk

  3. Frederick University, Nicosia, Cyprus

    Harris Papadopoulos

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Burnaev, E., Panov, M. (2015). Adaptive Design of Experiments Based on Gaussian Processes. In: Gammerman, A., Vovk, V., Papadopoulos, H. (eds) Statistical Learning and Data Sciences. SLDS 2015. Lecture Notes in Computer Science(), vol 9047. Springer, Cham. https://doi.org/10.1007/978-3-319-17091-6_7

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  • DOI: https://doi.org/10.1007/978-3-319-17091-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17090-9

  • Online ISBN: 978-3-319-17091-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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