Gaussian random fields (GRF) are a fundamental stochastic model for spatiotemporal data analysis. An essential ingredient of GRF is the covariance function that characterizes the joint Gaussian distribution of the field. Commonly used covariance functions give rise to fully dense and unstructured ...
An efficient approach is to specify GRFs via stochastic partial differential equations (SPDEs) and derive Gaussian Markov random field (GMRF) approximations of the solutions. We consider the construction of a class of non-stationary GRFs with varying local anisotropy, where the local anisotropy is...
A discrete Gaussian random field (GRF) model is a finite-dimensional Gaussian process (GP) whose prior covariance is the inverse of a graph Laplacian. Minimizing the trace of the predictive covariance Sigma (V-optimality) on GRFs has proven successful in batch active learning classification ...
The Gaussian random field method considers a stochastic random field in the three-dimensional space, which is constrained to have a number of statistical properties corresponding to the physical material. From: Studies in Surface Science and Catalysis, 2002 ...
Fitting a Gaussian Random Field (GRF) model to spatial data via maximum likelihood (ML) requires optimizing a highly non-convex function. Iterative methods... SD Tajbakhsh,NS Aybat,ED Castillo - 《Eprint Arxiv》 被引量: 6发表: 2014年 Online Regression for Data With Changepoints Using Gaussi...
gaussian random field (grf)gaussian process (gp)Due to the large data size of 3D MR brain images and the blurry boundary of the pathological tissues, ... Y Song,C Zhang,Jianguo Lee Fei Wang,... - 《Pattern Analysis & Applications》 被引量: 47发表: 2009年 Theoretical study of the X2...
The main task in studying the RGRF is to estimate an asymptotic upper bound of its excursion probability, which asymptotically converges to zero. In doing so, we establish a relationship between the RGRF and its netput Gaussian random field (GRF) via a Skorohod mapping. Then, as an ...
Gaussian random fieldnon negativitynon stationarityThis paper represents a survey of recent advances in modeling of space or space-time Gaussian Random Fields (GRF), tools of Geostatistics at hand for the understanding of special cases of noise in image analysis. They can be used when stationarity...
Gaussian random fieldBayes discriminant functionspatial correlationexpected error rate62H3062H1262H40We consider the problem of supervised classifying the multivariate Gaussian random field (GRF) single observation into one of two populations in case of given training sample. The populations are specified ...
Motivated from measuring the performance of quantum computing and storage systems together with nanorheology over different shapes of devices, we introduce a reflecting time-space Gaussian random field (RGRF) on a general (1+d) -parameter compact Riemannian manifold to model the quantum particle ...