Constructing a Numerically Statistical Model of a Homogeneous Random Field with a Given Distribution of the Integral over One of the Phase Coordinates

2019 ◽  
Vol 100 (3) ◽  
pp. 529-532
Author(s):  
G. A. Mikhailov ◽  
E. G. Kablukova ◽  
V. A. Ogorodnikov ◽  
S. M. Prigarin
Keyword(s):  
Statistical Model ◽  
Random Field ◽  
Homogeneous Random Field ◽  
Phase Coordinates

scholarly journals Constructing a numerically statistical model of a homogeneous random field with the given distribution of the integral over one of the phase coordinates

2019 ◽  
Vol 489 (2) ◽  
pp. 131-135
Author(s):  
G. A. Mikhailov ◽  
E. G. Kablukova ◽  
V. A. Ogorodnikov ◽  
S. M. Prigarin
Keyword(s):  
Random Field ◽  
Three Dimensional ◽  
Horizontal Layer ◽  
Homogeneous Random Field ◽  
Vertical Coordinate ◽  
One Dimensional ◽  
Basic Model ◽  
Phase Coordinates ◽  
Dimensional Distribution ◽  
The Given

The problem of constructing a numerically realizable model of a three-dimensional homogeneous random field in a horizontal layer 0 z H with given one-dimensional distribution and correlation function of the integral over vertical coordinate z is solved. An aggregate of n independent elementary horizontal layers of thickness h = H/n, vertically shifted by a random value uniformly distributed in the interval (0, h) is considered as a basic model.

A note on the estimation of the mean of a homogeneous random field

1971 ◽  
Vol 8 (3) ◽  
pp. 626-629
Author(s):  
Michael Skalsky
Keyword(s):  
Finite Number ◽  
Random Field ◽  
Random Process ◽  
Mathematical Expectation ◽  
Covariance Function ◽  
Arithmetic Mean ◽  
Stationary Random Process ◽  
Homogeneous Random Field ◽  
The Mean

An important problem, arising in connection with the estimation of mathematical expectation of a homogeneous random field X(x1, ···, xn) in Rn by means of the arithmetic mean of observed values, is to determine the number of observations for which the variance of the estimate attains its minimum. Vilenkin [2] has shown, that in the case of a stationary random process X(x) such a finite number exists, provided that the covariance function satisfies certain conditions.

SELF-INTERSECTION LOCAL TIME FOR ${\mathcal S}' ({\mathbb R}^d)$-WIENER PROCESSES AND RELATED ORNSTEIN–UHLENBECK PROCESSES

1999 ◽  
Vol 02 (04) ◽  
pp. 569-615 ◽  
Author(s):  
TOMASZ BOJDECKI ◽  
LUIS G. GOROSTIZA
Keyword(s):  
Random Field ◽  
Local Time ◽  
Fourier Transforms ◽  
Stable Process ◽  
Local Times ◽  
Spherically Symmetric ◽  
Homogeneous Random Field ◽  
Intersection Local Time ◽  
Wiener Processes ◽  
First Results

Existence and continuity results are obtained for self-intersection local time of [Formula: see text]-valued Ornstein–Uhlenbeck processes [Formula: see text], where X0 is Gaussian, Wt is an [Formula: see text]-Wiener process (independent of X0), and T't is the adjoint of a semigroup Tt on [Formula: see text]. Two types of covariance kernels for X0 and for W are considered: square tempered kernels and homogeneous random field kernels. The case where Tt corresponds to the spherically symmetric α-stable process in ℝd, α∈(0,2], is treated in detail. The method consists in proving first results for self-intersection local times of the ingredient processes: Wt, T't X0 and [Formula: see text], from which the results for Xt are derived. As a by-product, a class of non-finite tempered measures on ℝd whose Fourier transforms are functions is identified. The tools are mostly analytical.

Simulation of stochastic waves in a non-homogeneous random field

1995 ◽  
Vol 14 (5) ◽  
pp. 387-396 ◽  
Author(s):  
Junji Kiyono ◽  
Kenzo Toki ◽  
Tadanobu Sato ◽  
Haruhiro Mizutani
Keyword(s):  
Random Field ◽  
Homogeneous Random Field ◽  
Stochastic Waves

Automatic Segmentation of Breast MR Images Through a Markov Random Field Statistical Model

2014 ◽  
Vol 33 (10) ◽  
pp. 1986-1996 ◽  
Author(s):  
S. Ribes ◽  
D. Didierlaurent ◽  
N. Decoster ◽  
E. Gonneau ◽  
L. Risser ◽  
...  
Keyword(s):  
Statistical Model ◽  
Random Field ◽  
Markov Random Field ◽  
Automatic Segmentation ◽  
Mr Images ◽  
Markov Random ◽  
Breast Mr
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