Shot Noise Random Fields

1988 ◽  
pp. 423-427
Author(s):  
B. Bassan ◽  
E. Bona
Keyword(s):  
Shot Noise ◽  
Random Fields

scholarly journals On the perimeter of excursion sets of shot noise random fields

2016 ◽  
Vol 44 (1) ◽  
pp. 521-543 ◽  
Author(s):  
Hermine Biermé ◽  
Agnès Desolneux
Keyword(s):  
Shot Noise ◽  
Random Fields ◽  
Excursion Sets

Burgers' equation by non-local shot noise data

1994 ◽  
Vol 31 (A) ◽  
pp. 351-362 ◽  
Author(s):  
Donatas Surgailis ◽  
Wojbor A. Woyczynski
Keyword(s):  
Shot Noise ◽  
Random Fields ◽  
Burgers Equation ◽  
Initial Conditions ◽  
Scaling Limit ◽  
Linear Partial Differential Equation ◽  
Local Case ◽  
Non Linear ◽  
Noise Data ◽  
Non Local

We study the scaling limit of random fields which are solutions of a non-linear partial differential equation, known as the Burgers equation, under stochastic initial conditions. These are assumed to be of a non-local shot noise type and driven by a Cox process. Previous work by Bulinskii and Molchanov (1991), Surgailis and Woyczynski (1993a), and Funaki et al. (1994) concentrated on the case of local shot noise data which permitted use of techniques from the theory of random fields with finite range dependence. Those are not available for the non-local case being considered in this paper.Burgers' equation is known to describe various physical phenomena such as non-linear and shock waves, distribution of self-gravitating matter in the universe, and other flow satisfying conservation laws (see e.g. Woyczynski (1993)).

Burgers' equation by non-local shot noise data

1994 ◽  
Vol 31 (A) ◽  
pp. 351-362 ◽  
Author(s):  
Donatas Surgailis ◽  
Wojbor A. Woyczynski
Keyword(s):  
Shot Noise ◽  
Random Fields ◽  
Burgers Equation ◽  
Initial Conditions ◽  
Scaling Limit ◽  
Linear Partial Differential Equation ◽  
Local Case ◽  
Non Linear ◽  
Noise Data ◽  
Non Local

We study the scaling limit of random fields which are solutions of a non-linear partial differential equation, known as the Burgers equation, under stochastic initial conditions. These are assumed to be of a non-local shot noise type and driven by a Cox process. Previous work by Bulinskii and Molchanov (1991), Surgailis and Woyczynski (1993a), and Funaki et al. (1994) concentrated on the case of local shot noise data which permitted use of techniques from the theory of random fields with finite range dependence. Those are not available for the non-local case being considered in this paper. Burgers' equation is known to describe various physical phenomena such as non-linear and shock waves, distribution of self-gravitating matter in the universe, and other flow satisfying conservation laws (see e.g. Woyczynski (1993)).

Reduction of shot-noise in quantum conductors

1999 ◽  
Vol 09 (PR2) ◽  
pp. Pr2-23
Author(s):  
L. Saminadayar ◽  
A. Kumar ◽  
D. C. Glattli ◽  
Y. Jin ◽  
B. Etienne
Keyword(s):  
Shot Noise

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.

PRNU-based Image Manipulation Localization with Discriminative Random Fields

2017 ◽  
Vol 2017 (7) ◽  
pp. 113-120 ◽  
Author(s):  
Sujoy Chakraborty ◽  
Matthias Kirchner
Keyword(s):  
Random Fields ◽  
Image Manipulation
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