Results 211 to 220 of about 122,014 (258)

Uniform 2D Target Generation via Inverse-designed Metasurfaces. [PDF]

IEEE J Sel Top Quantum Electron
Zhou Y, Chen YS, Zhao Y.
europepmc   +1 more source

Frangipani, hibiscus, marigold, and rose leaf imageimagemagei dataset for plant species identification. [PDF]

Data Brief
Matin MMH, Sefatullah M.
europepmc   +1 more source

Gaussian Process Latent Random Field

Proceedings of the AAAI Conference on Artificial Intelligence, 2010
In this paper, we propose a novel supervised extension of GPLVM, called Gaussian process latent random field (GPLRF), by enforcing the latent variables to be a Gaussian Markov random field with respect to a graph constructed from the supervisory information.
Guoqiang Zhong 0001, Wu-Jun Li, Dit-Yan Yeung, Xinwen Hou, Cheng-Lin Liu 0001   +4 more
core   +3 more sources

Advances in Gaussian random field generation: a review

Computational Geosciences, 2019
Gaussian (normal) distribution is a basic continuous probability distribution in statistics, it plays a substantial role in scientific and engineering problems that related to stochastic phenomena.
Shuyu Sun, Sun Shuyu
exaly   +2 more sources

Random field effects on the anisotropic quantum Heisenberg model with Gaussian random magnetic field distribution

Physica A: Statistical Mechanics and Its Applications, 2013
The effect of a zero-centered Gaussian random magnetic field distribution on the phase transition properties of the anisotropic quantum Heisenberg model has been investigated on a honeycomb lattice within the framework of effective field theory (EFT) for
Umit Akinci
exaly   +2 more sources

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Fitting Gaussian Markov Random Fields to Gaussian Fields

Scandinavian Journal of Statistics, 2001
This paper discusses the following task often encountered in building Bayesian spatial models: construct a homogeneous Gaussian Markov random field (GMRF) on a lattice with correlation properties either as present in some observed data, or consistent with prior knowledge.
Rue, Håvard, Tjelmeland, Håkon
openaire   +2 more sources

Gaussian random fields

Applied Mathematics & Optimization, 1980
Nonanticipative representations of Gaussian random fields equivalent to the two-parameter Wiener process are defined, and necessary and sufficient conditions for their existence derived. When such representations exist they provide examples of canonical representations of multiplicity one. In contrast to the one-parameter case, examples are given where
Bromley, C., Kallianpur, Gopinath
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Structures in random fields: Gaussian fields

Physical Review A, 1992
We present two alternative methods for evaluating the probability densities of structures defined by d degrees of freedom in random fields. For Gaussian random fields, both differentiable and nondifferentiable, the application of these methods is considered in detail.
openaire   +2 more sources