Sampling large hyperplane-truncated multivariate normal distributions - Université Clermont Auvergne Accéder directement au contenu
Pré-Publication, Document De Travail Année : 2022

Sampling large hyperplane-truncated multivariate normal distributions

Résumé

Generating multivariate normal distributions is widely used in many fields (such as engineering). In this paper, simulating large multivariate normal distributions truncated on the intersection of a set of hyperplanes is investigated. The proposed methodology focuses on Gaussian vectors extracted from a Gaussian process (GP) in one dimension. It is based on combining both Karhunen-Loève expansions (KLE) and Matheron's update rules (MUR). The KLE requires the computation of the decomposition of the covariance matrix of the random variables. This step becomes expensive when the random vector is too large. To deal with this issue, the input domain is split in smallest subdomains where the eigendecomposition can be computed. By this strategy, the computational complexity is drastically reduced. The mean-square truncation and block errors have been calculated. Some numerical experiments are presented in order to study the efficiency of the proposed approach. Keywords Large scale random fields • hyperplane-truncated • Karhunen-Loève expansion • Matheron's update rule.
Fichier principal
Vignette du fichier
highdimMVN.pdf (652.82 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)

Dates et versions

hal-03741860 , version 1 (02-08-2022)
hal-03741860 , version 2 (19-09-2023)

Identifiants

  • HAL Id : hal-03741860 , version 1

Citer

Hassan Maatouk, Didier Rullière, Xavier Bay. Sampling large hyperplane-truncated multivariate normal distributions. 2022. ⟨hal-03741860v1⟩
140 Consultations
81 Téléchargements

Partager

Gmail Facebook X LinkedIn More