HAL will be down for maintenance from Friday, June 10 at 4pm through Monday, June 13 at 9am. More information
Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Variational Calibration of Computer Models

Abstract : Bayesian calibration of black-box computer models offers an established framework to obtain a posterior distribution over model parameters. Traditional Bayesian calibration involves the emulation of the computer model and an additive model discrepancy term using Gaussian processes; inference is then carried out using MCMC. These choices pose computational and statistical challenges and limitations, which we overcome by proposing the use of approximate Deep Gaussian processes and variational inference techniques. The result is a practical and scalable framework for calibration, which obtains competitive performance compared to the state-of-the-art.
Complete list of metadata

https://hal.archives-ouvertes.fr/hal-01906139
Contributor : Sébastien Marmin Connect in order to contact the contributor
Submitted on : Friday, October 26, 2018 - 2:33:49 PM
Last modification on : Tuesday, November 6, 2018 - 11:48:29 AM
Long-term archiving on: : Sunday, January 27, 2019 - 2:17:04 PM

Files

preprint.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-01906139, version 1
  • ARXIV : 1810.12177

Collections

Citation

Sébastien Marmin, Maurizio Filippone. Variational Calibration of Computer Models. 2018. ⟨hal-01906139⟩

Share

Metrics

Record views

32

Files downloads

99