Skip to Main content Skip to Navigation
Preprints, Working Papers, ...

Sampling large hyperplane-truncated multivariate normal distributions

Abstract : 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.
Complete list of metadata
Contributor : Hassan Maatouk Connect in order to contact the contributor
Submitted on : Tuesday, August 2, 2022 - 6:13:43 AM
Last modification on : Monday, September 5, 2022 - 5:02:18 PM


Files produced by the author(s)


  • HAL Id : hal-03741860, version 1


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



Record views


Files downloads