Variational Calibration of Computer Models - Archive ouverte HAL Access content directly
Preprints, Working Papers, ... Year : 2018

Variational Calibration of Computer Models

Sébastien Marmin
  • Function : Author
  • PersonId : 965099
Maurizio Filippone
  • Function : Author
  • PersonId : 1021042

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.
Fichier principal
Vignette du fichier
preprint.pdf (1018.9 Ko) Télécharger le fichier
Origin : Files produced by the author(s)

Dates and versions

hal-01906139 , version 1 (26-10-2018)

Identifiers

Cite

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

Collections

CNRS EURECOM
47 View
118 Download

Altmetric

Share

Gmail Facebook X LinkedIn More