Afficher la couverture 'Gearing Gaussian process modeling and sequential design towards stochastic simulators | Binois, Mickael' en grand format
/5

0 avis

Gearing Gaussian process modeling and sequential design towards stochastic simulators

Archive ouverte

Binois, Mickael | Fadikar, Arindam | Stevens, Abby

Edité par CCSD -

This chapter presents specific aspects of Gaussian process modeling in the presence of complex noise. Starting from the standard homoscedastic model, various generalizations from the literature are presented: input varying noise variance, non-Gaussian noise, or quantile modeling. These approaches are compared in terms of goal, data availability and inference procedure. A distinction is made between methods depending on their handling of repeated observations at the same location, also called replication. The chapter concludes with the corresponding adaptations of the sequential design procedures. These are illustrated in an example from epidemiology.

Suggestions

Du même auteur

Analyzing Stochastic Computer Models: A Review with Opportunities

Archive ouverte | Baker, Evan | CCSD

International audience. In modern science, computer models are often used to understand complex phenomena and a thriving statistical community has grown around analyzing them. This review aims to bring a spotlight t...

Heteroskedastic Gaussian processes for simulation experiments

Archive ouverte | Binois, Mickael | CCSD

International audience. An increasing number of time-consuming simulators exhibit a complex noise structure that depends on the inputs. To conduct studies with limited budgets of evaluations, new surrogate methods a...

A portfolio approach to massively parallel Bayesian optimization

Archive ouverte | Binois, Mickael | CCSD

International audience. One way to reduce the time of conducting optimization studies is to evaluate designs in parallel rather than just one-at-a-time. For expensive-to-evaluate black-boxes, batch versions of Bayes...

Chargement des enrichissements...