Goal-oriented adaptive sampling under random field modelling of response probability distributions

Gautier, Athénaïs; Ginsbourger, David; Pirot, Guillaume (August 2021). Goal-oriented adaptive sampling under random field modelling of response probability distributions. ESAIM: Proceedings and Surveys, 71, pp. 89-100. EDP Sciences 10.1051/proc/202171108

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In the study of natural and artificial complex systems, responses that are not completely determined by the considered decision variables are commonly modelled probabilistically, resulting in response distributions varying across decision space. We consider cases where the spatial variation of these response distributions does not only concern their mean and/or variance but also other features including for instance shape or uni-modality versus multi-modality. Our contributions build upon a non-parametric Bayesian approach to modelling the thereby induced fields of probability distributions, and in particular to a spatial extension of the logistic Gaussian model. The considered models deliver probabilistic predictions of response distributions at candidate points, allowing for instance to perform (approximate) posterior simulations of probability density functions, to jointly predict multiple moments and other functionals of target distributions, as well as to quantify the impact of collecting new samples on the state of knowledge of the distribution field of interest. In particular, we introduce adaptive sampling strategies leveraging the potential of the considered random distribution field models to guide system evaluations in a goal-oriented way, with a view towards parsimoniously addressing calibration and related problems from non-linear (stochastic) inversion and global optimisation.

Item Type:

Conference or Workshop Item (Paper)

Division/Institute:

08 Faculty of Science > Department of Mathematics and Statistics > Institute of Mathematical Statistics and Actuarial Science

UniBE Contributor:

Gautier, Athénaïs Honorine Alphonsine and Ginsbourger, David

Subjects:

300 Social sciences, sociology & anthropology > 360 Social problems & social services
500 Science > 510 Mathematics

ISSN:

2267-3059

Publisher:

EDP Sciences

Language:

English

Submitter:

David Ginsbourger

Date Deposited:

04 May 2022 16:10

Last Modified:

05 Dec 2022 16:18

Publisher DOI:

10.1051/proc/202171108

BORIS DOI:

10.48350/168972

URI:

https://boris.unibe.ch/id/eprint/168972

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