VarianceGammaPointProcess
VarianceGammaPointProcess[μ,λ,α,β,d]
represents a variance gamma cluster point process with density μ, cluster mean λ and shape parameters α and β in .
Details
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- VarianceGammaPointProcess models clustered point configurations with centers uniformly distributed over space and cluster points isotropically distributed with a flexible radial distribution.
- Typical uses include cosmology and rainforest tree distribution.
- The cluster centers are placed according to PoissonPointProcess with density μ.
- The point count of a cluster is distributed according to PoissonDistribution with mean λ.
- The cluster points in each cluster in
are distributed according to VarianceGammaDistribution[α,
,0,0].
- The cluster points in
are distributed according to MultinormalDistribution[DiagonalMatrix[{u,u,…}]], with
sampled from GammaDistribution[α,β] centered at a cluster center.
- VarianceGammaPointProcess allows μ, λ, α and β to be any positive real numbers, and d to be any positive integer.
- The following settings can be used for PointProcessEstimator for estimating VarianceGammaPointProcess:
-
"FindClusters" use FindClusters function "MethodOfMoments" use a homogeneity measure to estimate the parameters - VarianceGammaPointProcess can be used with such functions as RipleyK, PointCountDistribution and RandomPointConfiguration.
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Examples
open allclose allBasic Examples (4)
Scope (2)
Sample from any valid RegionQ, whose RegionEmbeddingDimension is equal to its RegionDimension:
Simulate a point configuration from a variance gamma point process:
Use the "FindClusters" method to estimated a point process model:
Compare Ripley's measure between the original process and the estimated model:
Properties & Relations (5)
The PointCountDistribution is known:
The probability density histogram:
Ripley's and Besag's
for a variance gamma point process in 2D:
Ripley's of a variance gamma point process is larger than for a Poisson point process:
Compare to a Poisson point process:
Besag's of a variance gamma point process is larger than for a Poisson point process:
Compare to a Poisson point process:
The pair correlation of a variance gamma point process is larger than 1:
Text
Wolfram Research (2020), VarianceGammaPointProcess, Wolfram Language function, https://reference.wolfram.com/language/ref/VarianceGammaPointProcess.html.
CMS
Wolfram Language. 2020. "VarianceGammaPointProcess." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/VarianceGammaPointProcess.html.
APA
Wolfram Language. (2020). VarianceGammaPointProcess. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/VarianceGammaPointProcess.html