Automatic kernel selection for Gaussian processes regression with approximate Bayesian computation and sequential Monte Carlo

Abdessalem, A.B., Dervilis, N., Wagg, D.J. et al. (1 more author) (2017) Automatic kernel selection for Gaussian processes regression with approximate Bayesian computation and sequential Monte Carlo. Frontiers in Built Environment, 3. 52.

Abstract

Metadata

Authors/Creators:
  • Abdessalem, A.B.
  • Dervilis, N.
  • Wagg, D.J.
  • Worden, K.
Copyright, Publisher and Additional Information: © 2017 Abdessalem, Dervilis, Wagg and Worden. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (https://creativecommons.org/licenses/by/4.0/). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
Keywords: Kernel selection; hyperparameter estimation; approximate Bayesian computation; sequential Monte Carlo; Gaussian processes
Dates:
  • Published (online): 30 August 2017
  • Accepted: 8 August 2017
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Mechanical Engineering (Sheffield)
Funding Information:
FunderGrant number
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC)EP/K003836/2
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC)EP/K003836/1
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL (EPSRC)EP/J016942/1
Depositing User: Symplectic Sheffield
Date Deposited: 31 Aug 2017 15:11
Last Modified: 31 Aug 2017 15:28
Published Version: https://doi.org/10.3389/fbuil.2017.00052
Status: Published
Publisher: Frontiers Media
Refereed: Yes
Identification Number: https://doi.org/10.3389/fbuil.2017.00052

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