Results 41 to 50 of about 57,267 (303)
The curvature effect in Gaussian random fields
Journal of Physics: Complexity, 2022 Abstract Random field models are mathematical structures used in the study of stochastic complex systems. In this paper, we compute the shape operator of Gaussian random field manifolds using the first and second fundamental forms (Fisher information matrices).
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Statistical classification based on observations of random Gaussian fields
Mathematical Modelling and Analysis, 1999 The problem of classification of objects located in domain D ⊂ R2 based on observations of random Gaussian fields with a factorized covariance function is considered.
J. Šaltyte, K. Dučinskas
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Gaussian Process Random Fields
CoRR, 2015 Gaussian processes have been successful in both supervised and unsupervised machine learning tasks, but their computational complexity has constrained practical applications. We introduce a new approximation for large-scale Gaussian processes, the Gaussian Process Random Field (GPRF), in which local GPs are coupled via pairwise potentials.
David A. Moore, Stuart J. Russell
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Snake based Unsupervised Texture Segmentation using Gaussian Markov Random Field Models [PDF]
, 2011 A functional for unsupervised texture segmentation is investigated in this paper. An auto-normal model based on Markov Random Fields is employed to model textures. The functional investigated here is optimized with respect to the model parameters and the
Mahmoodi, Sasan +3 more
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Water Resources Research, 2023 Inverse modeling of hydraulic tomography (HT) is computationally expensive for estimating high‐dimensional hydrogeologic parameter fields. In this work, we develop a novel method called HT‐INV‐NN, which combines dimensionality reduction techniques with a
Quan Guo, Ming Liu, Jian Luo
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Egyptian Informatics Journal, 2021 The analysis of human emotions plays a significant role in providing sufficient information about patients in monitoring their feelings for better management of their diseases.
Muhammad Hameed Siddiqi
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Ergodicity and Gaussianity for spherical random fields [PDF]
Journal of Mathematical Physics, 2010 We investigate the relationship between ergodicity and asymptotic Gaussianity of isotropic spherical random fields in the high-resolution (or high-frequency) limit. In particular, our results suggest that under a wide variety of circumstances the two conditions are equivalent, i.e., the sample angular power spectrum may converge to the population value
MARINUCCI, DOMENICO, Peccati, G.
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Bayesian mapping of brain regions using compound Markov random field priors [PDF]
, 2003 Human brain mapping, i.e. the detection of functional regions and their connections, has experienced enormous progress through the use of functional magnetic resonance imaging (fMRI).
Gössl, Christoff +2 more
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Learning in Gaussian Markov random fields [PDF]
2010 IEEE International Conference on Acoustics, Speech and Signal Processing, 2010 This paper addresses the problem of state estimation in the case where the prior distribution of the states is not perfectly known but instead is parameterized by some unknown parameter. Thus in order to support the state estimator with prior information on the states and improve the quality of the state estimates, it is necessary to learn this unknown
Thomas J. Riedl +2 more
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The risk of classification based on observations of anisotropic Gaussian random fields
Lietuvos Matematikos Rinkinys, 1998 There is not abstract.
Jūratė Šaltytė, Kęstutis Dučinskas
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