Results 41 to 50 of about 3,751,804 (256)
Screening effects in Gaussian random fields under generalized spectral conditions
Results in Applied MathematicsThe screening effect is a central idea in spatial prediction: once nearby observations are used, distant ones add little. While Stein’s classical results explain this effect under strong spectral conditions, many models used in practice fall outside ...
Mohammad Meysami, Ali Lotfi
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Characteristic Function of the Tsallis q-Gaussian and Its Applications in Measurement and Metrology
Metrology, 2023 The Tsallis q-Gaussian distribution is a powerful generalization of the standard Gaussian distribution and is commonly used in various fields, including non-extensive statistical mechanics, financial markets and image processing.
Viktor Witkovský
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Natural Frequencies of Levodopa‐Induced Dyskinesia in Parkinson's Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objectives Abnormal involuntary movements, known as dyskinesias, are common complications of levodopa treatment in patients with Parkinson's disease and can significantly impair quality of life. The underlying pathophysiology remains unclear, and current therapeutic options are limited.
Ioannis U. Isaias +3 more
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Classification of points in 2-dimensional space based on realizations of Gaussian random fields
Lietuvos Matematikos Rinkinys, 1999 There is not abstract.
Jūratė Šaltytė, Kęstutis Dučinskas
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On Fractional Gaussian Random Fields Simulations
Journal of Statistical Software, 2007 To simulate Gaussian fields poses serious numerical problems: storage and computing time. The midpoint displacement method is often used for simulating the fractional Brownian fields because it is fast.
Alexandre Brouste +2 more
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MethodsX, 2023 Stochastic field reconstruction is a crucial technique to improve the accuracy of modern rock simulation. It allows explicit modelling of field conditions, often employed in uncertainty quantification analysis and upsampling and upscaling procedures ...
Nathalia B. Guerra +4 more
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dynoGP: Deep Gaussian Processes for Dynamic System Identification
International Journal of Adaptive Control and Signal Processing, EarlyView.This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli +3 more
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The Sequential Generation of Gaussian Random Fields for Applications in the Geospatial Sciences
ISPRS International Journal of Geo-Information, 2014 This paper presents practical methods for the sequential generation or simulation of a Gaussian two-dimensional random field. The specific realizations typically correspond to geospatial errors or perturbations over a horizontal plane or grid. The errors
John Dolloff, Peter Doucette
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Applied Computing and Geosciences, 2023 In the present study, we co-simulate hydrofacies and piezometric data in order to construct geostatistical realizations of underground geology in an area of the West Thessaly basin.
George Valakas +2 more
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Polar sets of anisotropic Gaussian random fields [PDF]
, 2009 This paper studies polar sets of anisotropic Gaussian random fields, i.e. sets which a Gaussian random field does not hit almost surely. The main assumptions are that the eigenvalues of the covariance matrix are bounded from below and that the canonical ...
Söhl, Jakob
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