24 citations · 59 across the 13 of their papers we have counts for
4 papers · 1 filter
Optimal Uncertainty Quantification on moment class using canonical moments
Jerome Stenger, Fabrice Gamboa, Merlin Keller +1
We gain robustness on the quantification of a risk measurement by accounting for all sources of uncertainties tainting the inputs of a computer code. We evaluate the maximum quanti…
Validation of a computer code for the energy consumption of a building, with application to optimal electric bill pricing
M. Keller, G. Damblin, A. Pasanisi +4
In this paper, we propose a practical Bayesian framework for the calibration and validation of a computer code, and apply it to a case study concerning the energy consumption forec…
CaliCo: a R package for Bayesian calibration
Mathieu Carmassi, Pierre Barbillon, Matthieu Chiodetti +2
In this article, we present a recently released R package for Bayesian calibration. Many industrial fields are facing unfeasible or costly field experiments. These experiments are…
Bayesian calibration of a numerical code for prediction
Mathieu Carmassi, Pierre Barbillon, Merlin Keller +2
Field experiments are often difficult and expensive to make. To bypass these issues, industrial companies have developed computational codes. These codes intend to be representativ…