A mutual information based distance for multivariate Gaussian processes

2007 ◽  
Author(s):  
Jeroen Boets ◽  
Katrien De Cock ◽  
Bart De Moor
Keyword(s):  
Mutual Information ◽  
Gaussian Processes ◽  
Multivariate Gaussian

scholarly journals Joint segmentation of multivariate Gaussian processes using mixed linear models

2011 ◽  
Vol 55 (2) ◽  
pp. 1160-1170 ◽  
Author(s):  
F. Picard ◽  
E. Lebarbier ◽  
E. Budinskà ◽  
S. Robin
Keyword(s):  
Gaussian Processes ◽  
Linear Models ◽  
Mixed Linear Models ◽  
Multivariate Gaussian

scholarly journals Generalized Inferences about the Mean Vector of Several Multivariate Gaussian Processes

2015 ◽  
Vol 2015 ◽  
pp. 1-9
Author(s):  
Pilar Ibarrola ◽  
Ricardo Vélez
Keyword(s):  
Gaussian Processes ◽  
Confidence Region ◽  
Confidence Regions ◽  
Covariance Matrices ◽  
Nuisance Parameters ◽  
Vector Parameter ◽  
Multivariate Gaussian ◽  
The Mean ◽  
Unknown Vector ◽  
Mean Vector

We consider in this paper the problem of comparing the means of several multivariate Gaussian processes. It is assumed that the means depend linearly on an unknown vector parameterθand that nuisance parameters appear in the covariance matrices. More precisely, we deal with the problem of testing hypotheses, as well as obtaining confidence regions forθ. Both methods will be based on the concepts of generalizedpvalue and generalized confidence region adapted to our context.

Extreme values and crossings for the X2-Process and Other Functions of Multidimensional Gaussian Processes, by Reliability Applications

1980 ◽  
Vol 12 (3) ◽  
pp. 746-774 ◽  
Author(s):  
Georg Lindgren
Keyword(s):  
Gaussian Processes ◽  
Survival Probability ◽  
Extreme Values ◽  
Extreme Points ◽  
Linear Functions ◽  
Degree Polynomial ◽  
Engineering Sciences ◽  
Multivariate Gaussian ◽  
Safe Region ◽  
Image Position

Extreme values of non-linear functions of multivariate Gaussian processes are of considerable interest in engineering sciences dealing with the safety of structures. One then seeks the survival probability where X(t) = (X1(t), …, Xn(t)) is a stationary, multivariate Gaussian load process, and S is a safe region. In general, the asymptotic survival probability for large T-values is the most interesting quantity.By considering the point process formed by the extreme points of the vector process X(t), and proving a general Poisson convergence theorem, we obtain the asymptotic survival probability for a large class of safe regions, including those defined by the level curves of any second- (or higher-) degree polynomial in (x1, …, xn). This makes it possible to give an asymptotic theory for the so-called Hasofer-Lind reliability index, β = inf>x∉S ||x||, i.e. the smallest distance from the origin to an unsafe point.

Sign in / Sign up

Export Citation Format

Share Document