"Gaussian random field"의 두 판 사이의 차이
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− | == 메타데이터 == | + | ==메타데이터== |
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===위키데이터=== | ===위키데이터=== | ||
* ID : [https://www.wikidata.org/wiki/Q5527848 Q5527848] | * ID : [https://www.wikidata.org/wiki/Q5527848 Q5527848] | ||
+ | ===Spacy 패턴 목록=== | ||
+ | * [{'LOWER': 'gaussian'}, {'LOWER': 'random'}, {'LEMMA': 'field'}] | ||
+ | * [{'LOWER': 'gauss'}, {'LOWER': 'random'}, {'LEMMA': 'Field'}] |
2021년 2월 17일 (수) 01:05 기준 최신판
노트
위키데이터
- ID : Q5527848
말뭉치
- A Gaussian random field (GRF) is a random field involving Gaussian probability density functions of the variables.[1]
- 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.[2]
- Known results are extended from the finite‐dimensional case to the dimension‐free case; hence, in particular, to Gaussian random fields.[3]
- 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.[4]
- Clipped Gaussian random fields can be used for modeling discrete-valued random fields with a given correlation structure.[5]
- 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.[6]
- 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.[6]
- 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.[6]
- 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.[6]
- We now review how probability distributions work for continuous, Gaussian random fields.[7]
- 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.[8]
- VoI can be computed easily through updating a Gaussian random field, i.e., kriging, which is a probabilistic interpolation method.[9]
소스
- ↑ Gaussian random field
- ↑ Gaussian Random Fields
- ↑ Realization of Gaussian random fields
- ↑ 8 Simulation of Gaussian Random Fields
- ↑ Gaussian RF
- ↑ 6.0 6.1 6.2 6.3 Fast generation of isotropic Gaussian random fields on the sphere
- ↑ Gaussian Fields
- ↑ Anderes , Stein : Estimating deformations of isotropic Gaussian random fields on the plane
- ↑ ASCE-ASME Journal of Risk and Uncertainty in Engineering Systems, Part A: Civil Engineering
메타데이터
위키데이터
- ID : Q5527848
Spacy 패턴 목록
- [{'LOWER': 'gaussian'}, {'LOWER': 'random'}, {'LEMMA': 'field'}]
- [{'LOWER': 'gauss'}, {'LOWER': 'random'}, {'LEMMA': 'Field'}]