Random vectors and random fields in high dimension. Parametric model-based representation, identification from data, and inverse problems

The notion of local nondeterminism has been extended to SαS processes and random fields by Nolan (1988, 1989), and has proven to be a useful tool in studying the local times

In this presentation, we propose and review the most recent advances in the construction and random generation of prior algebraic probabilistic representations for random fields

In this work, we address the construction of a generalized nonparametric probabilistic model for matrix- valued non-Gaussian random fields exhibiting some invariance properties

Based on the stochastic modeling of the non- Gaussian non-stationary vector-valued track geometry random field, realistic track geometries, which are representative of the

The steps of the methodology are the fol- lowing: (1) introduction of a family of Prior Algebraic Stochastic Model (PASM), (2) identification of an optimal PASM in the

A complete method- ology is proposed to solve this challenging problem and consists in introducing a family of prior probability models, in identifying an optimal prior model in

We have proposed a method to effectively construct the probability density function of a random variable in high dimension and for any support of its probability distribution by

Abstract. This paper is concerned with the derivation of a generic sampling technique for a class of non-Gaussian vector-valued random fields. Such an issue typically arises