[PDF] Top 20 Algorithms for stationary Gaussian random field generation
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Algorithms for stationary Gaussian random field generation
... second-order stationary field which is a lognormal distribution, with an isotropic exponential correlation ...the algorithms to generate Gaussian random ...the algorithms based ... Voir le document complet
17
GENFIELD: A parallel software for the generation of stationary Gaussian random fields
... order stationary field which has a lognormal distribution, with an isotropic exponential covariance ...generate Gaussian random fields based on the Circulant Embedding method and its ... Voir le document complet
2
Estimation of Space Deformation Model for Non-stationary Random Functions
... the Random Function in order to obtain an estimated sample covariance or vari- ogram ...natural field is unrealistic because there are not multiple parallel physical ...a Random Function, obtained as ... Voir le document complet
18
On ANOVA decompositions of kernels and Gaussian random field paths
... and Gaussian process modelling have proven efficient in a number of classification and prediction problems, finding a suitable kernel for a given application is often judged ...standard stationary ... Voir le document complet
16
Markov Random Field Model for Single Image Defogging
... Fig. 3. Defogging results on synthetic images from FRIDA2 database. First line: the image with homogeneous fog and Gaussian noise. Second line: the obtained restored images using proposed MRF model. Notice how ... Voir le document complet
6
Analysis of continuous spectral method for sampling stationary Gaussian random fields
... estimate for both strong and weak error of ...efficient for widely class of spectral density model used in the practice in which the Gaussian random field and its spectral density are ... Voir le document complet
26
On ANOVA Decompositions of Kernels and Gaussian Random Field Paths
... and Gaussian process modelling have proven efficient in a number of classification and prediction problems, finding a suitable kernel for a given application is often judged ...standard stationary ... Voir le document complet
15
Sequential Random Distortion Testing of Non-Stationary Processe
... Abstract: Random distortion testing (RDT) [ 1 ] addresses the problem of testing whether or not a random signal, Ξ, deviates by more than a specified tolerance, τ, from a fixed value, ξ 0 ...test for ... Voir le document complet
26
Gaussian fluctuations for linear spectral statistics of large random covariance matrices
... Empirical random covariance matrices, whose probabilistic study may be traced back to Wishart [ 57 ] in the late twenties, play an important role in applied ...fluctuations for linear spectral statistics of ... Voir le document complet
53
Latin Hypercube Sampling of Gaussian random field for Sobol' global sensitivity analysis of models with spatial inputs and scalar output
... Sampling; Gaussian random field; unconditionnal simulation ...tools for the modeller: they allow robustness of model predictions to be checked and help identifying the input factors that ... Voir le document complet
5
Comparison and anti-concentration bounds for maxima of Gaussian random vectors
... inequality for maxima of Gaussian random vectors, which is another main result of this paper (see Comment 4 for what anti-concentration inequalities here precisely refer to and how they differ ... Voir le document complet
23
A Generalized Convolution Model and Estimation for Non-stationary Random Functions
... into stationary domains, thereby increasing the required professional time for modelling and potentially producing disjointed domains that are globally ... Voir le document complet
25
Object grammars and random generation
... Uniform random generation, object grammars, ☎ -equations 1 Introduction An object grammar defines classes of objects by means of terminal objects and certain types of operations applied to the ...pictures. ... Voir le document complet
16
Automatic Generation of Detection Algorithms for Design Defects
... We attempt to overcome the limitations of previ- ous work on the detection of design defects by defining these defects synthetically and by generating detection algorithms automatically. We enhance the specifica- ... Voir le document complet
4
Ground States for a Stationary Mean-Field Model for a Nucleon
... To prove this theorem, we are going to apply a concentration-compactness lemma that we state below. The reader may refer to [3] and [4] for more details on this kind of approach. The particular shape of the energy ... Voir le document complet
15
Quantifying Studies of (Pseudo) Random Number Generation for Cryptography
... a random number generator which produces sequences from parameters that we cannot ...the random number generator of the Netscape browser used for the Secure Sockets Layer (SSL) ...of random ... Voir le document complet
190
Age-related differences in random generation
... a random generation task similar to that conducted in Experiment ...a random se- quence, at each of three production rates (4, 2, and 1 ...correlations for five of the six scores were ... Voir le document complet
16
Additivity and Ortho-Additivity in Gaussian Random Fields
... suitable for predictions if the data is generated by any other ...allowances for additive data cannot depict the complexity of some (more general) ... Voir le document complet
59
New alternate ring-coupled map for multi-random number generation
... 4 Conclusion Classical and emergent applications (chaotic encryp- tion, global warming, multi-agent competition) re- quire efficient PRNG generating independent and multi-random sequences. A new alternate ring- ... Voir le document complet
8
Pseudo-Random Number Generation on GP-GPU
... these random streams produced on GP-GPU? And more importantly: how can we ensure that they are independent? These two main problems led us to think about design guidelines that will hopefully help developers to ... Voir le document complet
16
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