scholarly journals From extreme values of i.i.d. random fields to extreme eigenvalues of finite-volume Anderson Hamiltonian

2016 ◽  
Vol 13 (0) ◽  
pp. 156-244 ◽  
Author(s):  
Arvydas Astrauskas
Keyword(s):  
Finite Volume ◽  
Random Fields ◽  
Extreme Values ◽  
Extreme Eigenvalues ◽  
Anderson Hamiltonian

Finite volume and spectral methods applied to Pb-30%TI alloy solidification

2001 ◽  
Vol 11 (PR6) ◽  
pp. Pr6-151-Pr6-159 ◽  
Author(s):  
R. Guérin ◽  
M. El Ganaoui ◽  
P. Haldenwang ◽  
P. Bontoux
Keyword(s):  
Finite Volume ◽  
Spectral Methods ◽  
Ti Alloy ◽  
Alloy Solidification

Comparison of Nonlinear Spatial Correlation Models by the Influence of the Data Augmentation to the Classification Risk

2002 ◽  
Vol 7 (1) ◽  
pp. 31-42
Author(s):  
J. Šaltytė ◽  
K. Dučinskas
Keyword(s):  
Spatial Correlation ◽  
Random Fields ◽  
Data Augmentation ◽  
Gaussian Random Fields ◽  
Classification Rule ◽  
Numerical Comparison ◽  
First Order ◽  
Bayesian Risk ◽  
Correlation Models

The Bayesian classification rule used for the classification of the observations of the (second-order) stationary Gaussian random fields with different means and common factorised covariance matrices is investigated. The influence of the observed data augmentation to the Bayesian risk is examined for three different nonlinear widely applicable spatial correlation models. The explicit expression of the Bayesian risk for the classification of augmented data is derived. Numerical comparison of these models by the variability of Bayesian risk in case of the first-order neighbourhood scheme is performed.

Sign in / Sign up

Export Citation Format

Share Document