Modelling calibration uncertainty in networks of environmental sensors

Smith, M.T., Ross, M., Ssematimba, J. et al. (3 more authors) (2023) Modelling calibration uncertainty in networks of environmental sensors. Journal of the Royal Statistical Society Series C: Applied Statistics, 72 (5). pp. 1187-1209. ISSN 0035-9254

Abstract

Metadata

Item Type: Article
Authors/Creators:
  • Smith, M.T.
  • Ross, M.
  • Ssematimba, J.
  • Álvarez, M.A.
  • Bainomugisha, E.
  • Wilkinson, R.
Copyright, Publisher and Additional Information:

© 2023 Oxford University Press. This is an author-produced version of a paper subsequently published in Journal of the Royal Statistical Society Series C: Applied Statistics. Uploaded in accordance with the publisher's self-archiving policy.

Keywords: air pollution; Bayesian modelling; calibration; Gaussian processes; low-cost sensors; variational inference
Dates:
  • Published: November 2023
  • Published (online): 24 August 2023
  • Accepted: 17 July 2023
Institution: The University of Sheffield
Academic Units: The University of Sheffield > Faculty of Engineering (Sheffield) > Department of Computer Science (Sheffield)
Funding Information:
Funder
Grant number
ENGINEERING AND PHYSICAL SCIENCE RESEARCH COUNCIL
EP/T00343X/1
GOOGLE.ORG
Google AirQo Project
Depositing User: Symplectic Sheffield
Date Deposited: 15 Aug 2023 16:41
Last Modified: 04 Oct 2024 14:16
Status: Published
Publisher: Oxford University Press
Refereed: Yes
Identification Number: 10.1093/jrsssc/qlad075
Open Archives Initiative ID (OAI ID):

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