Results 1  10
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136,511
Gaussian random number generators
 ACM Computing Surveys
, 2007
"... Rapid generation of high quality Gaussian random numbers is a key capability for simulations across a wide range of disciplines. Advances in computing have brought the power to conduct simulations with very large numbers of random numbers and with it, the challenge of meeting increasingly stringent ..."
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Cited by 24 (2 self)
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Rapid generation of high quality Gaussian random numbers is a key capability for simulations across a wide range of disciplines. Advances in computing have brought the power to conduct simulations with very large numbers of random numbers and with it, the challenge of meeting increasingly stringent
SkewGaussian Random Fields
, 2012
"... 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 skewGaussian random field is considered. The skewGaussian random field is constructed by using the multivariate closed skewnormal distrib ..."
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Cited by 1 (1 self)
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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 skewGaussian random field is considered. The skewGaussian random field is constructed by using the multivariate closed skew
On Expected Gaussian Random Determinants
"... The expectation of random determinants whose entries are realvalued, identically distributed, mean zero, correlated Gaussian random variables are examined using the Kronecker tensor products and some combinatorial arguments. This result is used to derive the expected determinants of X +B and AX +X ..."
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The expectation of random determinants whose entries are realvalued, identically distributed, mean zero, correlated Gaussian random variables are examined using the Kronecker tensor products and some combinatorial arguments. This result is used to derive the expected determinants of X +B and AX +X
Singularity in Gaussian random fields
 J. Theor. Probab
, 1993
"... In this paper we discuss a Gaussian random field that arises in pattern analysis. This random field exhibits phase transitive behavior for a particular value of the temperature parameter. We analyze this kind of non singular behavior and the effect that it has on the field random variables. The limi ..."
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Cited by 1 (1 self)
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In this paper we discuss a Gaussian random field that arises in pattern analysis. This random field exhibits phase transitive behavior for a particular value of the temperature parameter. We analyze this kind of non singular behavior and the effect that it has on the field random variables
On the structure of Gaussian random variables
, 2009
"... We study when a given Gaussian random variable on a given probability space (Ω, F, P) is equal almost surely to β1 where β is a Brownian motion defined on the same (or possibly extended) probability space. As a consequence of this result, we prove that the distribution of a random variable in a fini ..."
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Cited by 2 (2 self)
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We study when a given Gaussian random variable on a given probability space (Ω, F, P) is equal almost surely to β1 where β is a Brownian motion defined on the same (or possibly extended) probability space. As a consequence of this result, we prove that the distribution of a random variable in a
Set of Gaussian Random Variables
"... Abstract—This paper quantifies the approximation error when results obtained by Clark (Oper. Res., vol. 9, p. 145, 1961) are employed to compute the maximum (max) of Gaussian random variables, which is a fundamental operation in statistical timing. We show that a finite lookup table can be used to s ..."
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Abstract—This paper quantifies the approximation error when results obtained by Clark (Oper. Res., vol. 9, p. 145, 1961) are employed to compute the maximum (max) of Gaussian random variables, which is a fundamental operation in statistical timing. We show that a finite lookup table can be used
SemiSupervised Learning Using Gaussian Fields and Harmonic Functions
 IN ICML
, 2003
"... An approach to semisupervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The learning ..."
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Cited by 752 (14 self)
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An approach to semisupervised learning is proposed that is based on a Gaussian random field model. Labeled and unlabeled data are represented as vertices in a weighted graph, with edge weights encoding the similarity between instances. The learning
Numerical Simulation of NonGaussian Random
"... The nonGaussian random fields are used to modelling some dynamic loads generated by wind turbulence, ocean waves, earthquake ground motion etc. These fields also represent the uncertain properties of different materials (reinforced concrete, composite, soils etc.). This paper presents some methods ..."
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The nonGaussian random fields are used to modelling some dynamic loads generated by wind turbulence, ocean waves, earthquake ground motion etc. These fields also represent the uncertain properties of different materials (reinforced concrete, composite, soils etc.). This paper presents some
Fast simulation of Gaussian random fields
 In: Monte Carlo Methods Appl
"... Abstract. Fast Fourier transforms are used to develop algorithms for the fast generation of correlated Gaussian random fields on rectangular regions of Rd. The complexities of the algorithms are derived, simulation results and error analysis are presented. 1. ..."
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Cited by 3 (1 self)
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Abstract. Fast Fourier transforms are used to develop algorithms for the fast generation of correlated Gaussian random fields on rectangular regions of Rd. The complexities of the algorithms are derived, simulation results and error analysis are presented. 1.
ON DYNAMICAL GAUSSIAN RANDOM WALKS
"... Abstract. Motivated by the recent work of Benjamini, Häggström, Peres, and Steif (2003) on dynamical random walks, we: (i) Prove that, after a suitable normalization, the dynamical Gaussian walk converges weakly to the Ornstein–Uhlenbeck process in classical Wiener space; (ii) derive sharp tailasymp ..."
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Cited by 3 (0 self)
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Abstract. Motivated by the recent work of Benjamini, Häggström, Peres, and Steif (2003) on dynamical random walks, we: (i) Prove that, after a suitable normalization, the dynamical Gaussian walk converges weakly to the Ornstein–Uhlenbeck process in classical Wiener space; (ii) derive sharp
Results 1  10
of
136,511