Results 271 to 280 of about 57,267 (303)
Extremes of a class of nonhomogeneous Gaussian random fields
Annals of Probability, 2016 This contribution establishes exact tail asymptotics of sup(s,t)∈E X(s,t) for a large class of nonhomogeneous Gaussian random fields X on a bounded convex set E⊂R2, with variance function that attains its maximum on a segment on E.
Krzysztof Debicki +2 more
exaly +2 more sources
Some of the next articles are maybe not open access.
Related searches:
Related searches:
Fitting Gaussian Markov Random Fields to Gaussian Fields
Scandinavian Journal of Statistics, 2001This paper discusses the following task often encountered in building Bayesian spatial models: construct a homogeneous Gaussian Markov random field (GMRF) on a lattice with correlation properties either as present in some observed data, or consistent with prior knowledge.
Rue, Håvard, Tjelmeland, Håkon
openaire +2 more sources
Applied Mathematics & Optimization, 1980
Nonanticipative representations of Gaussian random fields equivalent to the two-parameter Wiener process are defined, and necessary and sufficient conditions for their existence derived. When such representations exist they provide examples of canonical representations of multiplicity one. In contrast to the one-parameter case, examples are given where
Bromley, C., Kallianpur, Gopinath
openaire +1 more source
Nonanticipative representations of Gaussian random fields equivalent to the two-parameter Wiener process are defined, and necessary and sufficient conditions for their existence derived. When such representations exist they provide examples of canonical representations of multiplicity one. In contrast to the one-parameter case, examples are given where
Bromley, C., Kallianpur, Gopinath
openaire +1 more source
Structures in random fields: Gaussian fields
Physical Review A, 1992We present two alternative methods for evaluating the probability densities of structures defined by d degrees of freedom in random fields. For Gaussian random fields, both differentiable and nondifferentiable, the application of these methods is considered in detail.
openaire +2 more sources
Simulation of parameterized random fields, Part II: Non-Gaussian cases
Mechanical Systems and Signal ProcessingThis paper presents two numerical algorithms to simulate non-Gaussian random fields that are parameterized by random parameters. The simulation of such kind of random fields is very challenging due to their parameterized non-Gaussian properties. For each
Zhibao Zheng +2 more
exaly +2 more sources
Gaussian Process Latent Random Field
Proceedings of the AAAI Conference on Artificial Intelligence, 2010In this paper, we propose a novel supervised extension of GPLVM, called Gaussian process latent random field (GPLRF), by enforcing the latent variables to be a Gaussian Markov random field with respect to a graph constructed from the supervisory information.
Guoqiang Zhong 0001 +4 more
openaire +1 more source
Foundations of Physics, 1978
Two results on Gaussian random fields are presented. The first characterizes the unit Gaussian random field by a strong independence property and the second determines Gaussian random fields that are generated by stochastic processes.
openaire +1 more source
Two results on Gaussian random fields are presented. The first characterizes the unit Gaussian random field by a strong independence property and the second determines Gaussian random fields that are generated by stochastic processes.
openaire +1 more source
2020
Gaussian random fields have a long history in science that dates back to the research of Andrey Kolmogorov and his group. Their investigation remains an active field of research with many applications in physics and engineering. The widespread appeal of Gaussian random fields is due to convenient mathematical simplifications that they enable, such as ...
openaire +1 more source
Gaussian random fields have a long history in science that dates back to the research of Andrey Kolmogorov and his group. Their investigation remains an active field of research with many applications in physics and engineering. The widespread appeal of Gaussian random fields is due to convenient mathematical simplifications that they enable, such as ...
openaire +1 more source
Local Additive Functionals of Gaussian Random Fields
Theory of Probability & Its Applications, 1984Translation from Teor. Veroyatn. Primen. 28, No.1, 32-44 (Russian) (1983; Zbl 0521.60059).
Dobrushin, R. L., Kel'bert, M. Ya.
openaire +2 more sources
Latin Hypercube Sampling of Gaussian Random Fields
Technometrics, 1999Following the method of Stein, this article shows how a Latin hypercube sample can be drawn from a Gaussian random field. In a case study the efficiency of Latin hypercube sampling is compared experimentally to that of simple random sampling. The model outputs studied are the mean and the 5- and 95-percentile of the areal fraction where point ...
Pebesma, E.J., Heuvelink, G.B.M.
openaire +2 more sources