Results 1 to 10 of about 122,014 (258)
Journal of Computational and Applied Mathematics, 2009 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
M. T. Alodat, Mohammad Y. Al-Rawwash
exaly +3 more sources
Optimization of Gaussian Random Fields [PDF]
SIAM Journal on Scientific Computing, 2015 Many engineering systems are subject to spatially distributed uncertainty, i.e. uncertainty that can be modeled as a random field. Altering the mean or covariance of this uncertainty will in general change the statistical distribution of the system outputs.
Dow, Eric, Wang, Qiqi
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Gaussian conditional random fields for classification
Expert Systems with Applications, 2023 Draft paper without experimental ...
Andrija Petrović +3 more
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Skew-Gaussian random fields [PDF]
Spatial Statistics, 2014 Skewness is often present in a wide range of spatial prediction problems, and modeling it in the spatial context remains a challenging problem. In this study a skew-Gaussian random field is considered. The skew-Gaussian random field is constructed by using the multivariate closed skew-normal distribution, which is a generalization of the traditional ...
Kjartan Rimstad, Henning Omre
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Simulation of Gaussian random field in a ball [PDF]
Monte Carlo Methods and Applications, 2022 Abstract We address the problem of statistical simulation of a scalar real Gaussian random field inside the unit 3D ball. Two different methods are studied: (i) the method based on the known homogeneous isotropic power spectrum developed by Meschede and Romanowicz [M. Meschede and B.
Dmitriy Kolyukhin, Alexander Minakov
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Fast simulation of Gaussian random fields [PDF]
Monte Carlo Methods and Applications, 2011 15 pages, 8 figures. Typos corrected in Algorithm 3, Remark (4), Algorithm 4, Remark (5), and Algorithm 5, Remark (5)
Annika Lang, Jürgen Potthoff
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Multiple points of Gaussian random fields [PDF]
Electronic Journal of Probability, 2021 This paper is concerned with the existence of multiple points of Gaussian random fields. Under the framework of Dalang et al. (2017), we prove that, for a wide class of Gaussian random fields, multiple points do not exist in critical dimensions. The result is applicable to fractional Brownian sheets and the solutions of systems of stochastic heat and ...
Dalang, Robert C. +3 more
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Extremes of Homogeneous Gaussian Random Fields [PDF]
Journal of Applied Probability, 2015 Let {X(s, t): s, t ≥ 0} be a centred homogeneous Gaussian field with almost surely continuous sample paths and correlation function r(s, t) = cov(X(s, t), X(0, 0)) such that r(s, t) = 1 - |s|α1 - |t|α2 + o(|s|α1 + |t|α2), s, t → 0, with α1, α2 ∈ (0, 2], and r(s, t) < 1 for (s, t) ≠ (0, 0). In this contribution we derive an asymptotic expansion (as u
Dębicki, Krzysztof +2 more
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Gaussian Fields and Random Packing
Journal of Statistical Physics, 2003 Consider sequential packing of unit balls in a large cube, as in the Renyi car-parking model, but in any dimension and with Poisson input. We show after suitable rescaling that the spatial distribution of packed balls tends to that of a Gaussian field in the thermodynamic limit. We prove analogous results for related applied models, including ballistic
Baryshnikov, Yu., Yukich, J. E.
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Deep Gaussian Markov Random Fields
CoRR, 2020 Gaussian Markov random fields (GMRFs) are probabilistic graphical models widely used in spatial statistics and related fields to model dependencies over spatial structures. We establish a formal connection between GMRFs and convolutional neural networks (CNNs).
Per Sidén, Fredrik Lindsten
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