Gaussian random field

노트

위키데이터

• ID : Q5527848

말뭉치

1. A Gaussian random field (GRF) is a random field involving Gaussian probability density functions of the variables.
2. The simplifications achieved by Gaussian random fields are based on fact that the joint Gaussian probability density function is fully determined by the mean and the covariance function.
3. Known results are extended from the finite‐dimensional case to the dimension‐free case; hence, in particular, to Gaussian random fields.
4. we recommend the package RandomFields (http://cran.r-project.org/src/contrib/PACKAGES.html#RandomFields) for a more comprehensive implementation for simulation of Gaussian Random Fields.
5. Clipped Gaussian random fields can be used for modeling discrete-valued random fields with a given correlation structure.
6. The computational complexity of realising an n × n lattice of points of a Gaussian random field in ℝ d depends considerably upon the structure of the covariance function.
7. On 𝕊 2 , similar results for isotropic Gaussian random fields also apply, i.e., we can perform a spectral decomposition into the spherical harmonic functions.
8. These can be sampled together with the derivatives point by point and then transformed to an isotropic Gaussian random field on the unit sphere by FFT.
9. In Section 2, we derive the decomposition of an isotropic Gaussian random field into 1d GMRFs via Fourier transforms, and compute the conditional covariance matrices.
10. We now review how probability distributions work for continuous, Gaussian random fields.
11. This paper presents a new approach to the estimation of the deformation of an isotropic Gaussian random field on ℝ2 based on dense observations of a single realization of the deformed random field.
12. VoI can be computed easily through updating a Gaussian random field, i.e., kriging, which is a probabilistic interpolation method.

메타데이터

위키데이터

• ID : Q5527848