scholarly journals Exact simulation of Brown-Resnick random fields at a finite number of locations

Extremes ◽  
2015 ◽  
Vol 18 (2) ◽  
pp. 301-314 ◽  
Author(s):  
A. B. Dieker ◽  
T. Mikosch
Keyword(s):  
Finite Number ◽  
Random Fields ◽  
Exact Simulation

Fast and exact simulation of Gaussian random fields defined on the sphere cross time

2019 ◽  
Vol 30 (1) ◽  
pp. 187-194 ◽  
Author(s):  
Francisco Cuevas ◽  
Denis Allard ◽  
Emilio Porcu
Keyword(s):  
Random Fields ◽  
Gaussian Random Fields ◽  
Exact Simulation

scholarly journals Exact simulation of coupled Wright–Fisher diffusions

2021 ◽  
Vol 53 (4) ◽  
pp. 923-950
Author(s):  
Celia García-Pareja ◽  
Henrik Hult ◽  
Timo Koski
Keyword(s):  
Finite Number ◽  
Simulation Study ◽  
Sampling Strategy ◽  
Coupling Term ◽  
Exact Simulation ◽  
Exact Sampling ◽  
Rejection Algorithm

AbstractIn this paper an exact rejection algorithm for simulating paths of the coupled Wright–Fisher diffusion is introduced. The coupled Wright–Fisher diffusion is a family of multivariate Wright–Fisher diffusions that have drifts depending on each other through a coupling term and that find applications in the study of networks of interacting genes. The proposed rejection algorithm uses independent neutral Wright–Fisher diffusions as candidate proposals, which are only needed at a finite number of points. Once a candidate is accepted, the remainder of the path can be recovered by sampling from neutral multivariate Wright–Fisher bridges, for which an exact sampling strategy is also provided. Finally, the algorithm’s complexity is derived and its performance demonstrated in a simulation study.

Fast and exact simulation of univariate and bivariate Gaussian random fields

Stat ◽  
2018 ◽  
Vol 7 (1) ◽  
pp. e188 ◽  
Author(s):  
Olga Moreva ◽  
Martin Schlather
Keyword(s):  
Random Fields ◽  
Gaussian Random Fields ◽  
Exact Simulation

scholarly journals On logarithmically optimal exact simulation of max-stable and related random fields on a compact set

Bernoulli ◽  
2019 ◽  
Vol 25 (4A) ◽  
pp. 2949-2981 ◽  
Author(s):  
Zhipeng Liu ◽  
Jose H. Blanchet ◽  
A.B. Dieker ◽  
Thomas Mikosch
Keyword(s):  
Random Fields ◽  
Compact Set ◽  
Exact Simulation

Criteria and Methods for Reliable Three-Dimensional Image Reconstruction

1974 ◽  
Vol 32 ◽  
pp. 330-331
Author(s):  
R. A. Crowther
Keyword(s):  
Image Reconstruction ◽  
Finite Number ◽  
Density Distribution ◽  
Electron Micrographs ◽  
Mathematical Problem ◽  
Three Dimensional ◽  
Ideal Case ◽  
Dimensional Image ◽  
General Object ◽  
The Ideal

The reconstruction of a three-dimensional image of a specimen from a set of electron micrographs reduces, under certain assumptions about the imaging process in the microscope, to the mathematical problem of reconstructing a density distribution from a set of its plane projections.In the absence of noise we can formulate a purely geometrical criterion, which, for a general object, fixes the resolution attainable from a given finite number of views in terms of the size of the object. For simplicity we take the ideal case of projections collected by a series of m equally spaced tilts about a single axis.

Comparison of Nonlinear Spatial Correlation Models by the Influence of the Data Augmentation to the Classification Risk

2002 ◽  
Vol 7 (1) ◽  
pp. 31-42
Author(s):  
J. Šaltytė ◽  
K. Dučinskas
Keyword(s):  
Spatial Correlation ◽  
Random Fields ◽  
Data Augmentation ◽  
Gaussian Random Fields ◽  
Classification Rule ◽  
Numerical Comparison ◽  
First Order ◽  
Bayesian Risk ◽  
Correlation Models

The Bayesian classification rule used for the classification of the observations of the (second-order) stationary Gaussian random fields with different means and common factorised covariance matrices is investigated. The influence of the observed data augmentation to the Bayesian risk is examined for three different nonlinear widely applicable spatial correlation models. The explicit expression of the Bayesian risk for the classification of augmented data is derived. Numerical comparison of these models by the variability of Bayesian risk in case of the first-order neighbourhood scheme is performed.

Sign in / Sign up

Export Citation Format

Share Document