BinomialPointProcess
BinomialPointProcess[n,reg]
represents a binomial point process with n points in the region reg.
Details
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- BinomialPointProcess[n,reg] generates points that are uniformly distributed in reg with the total number of points equal to n.
- Typical uses include cases where the total number of points is specified, such as a known number of wireless transceivers in a region, number of fish in an aquarium, etc.
- BinomialPointProcess and PoissonPointProcess both yield a uniform distribution of points over a region. The former uses a deterministic number of points and the latter a random number of points.
- The number of points inside a bounded subregion
follows BinomialDistribution[n,p] with p=RegionMeasure[sreg]/RegionMeasure[reg].
- The number of points in disjoint subregions of reg for a binomial point process is not independent.
- For disjoint subregions
that satisfy
, the joint distribution of the number of points inside the subregions
with volume
follows MultinomialDistribution[n,{ν1/ν,…,νn/ν}], where
is the volume of reg.
- BinomialPointProcess[n,reg] is PoissonPointProcess[μ] conditioned on the event
with
a random variable following PointCountDistribution[PoissonPointProcess[μ],reg].
- BinomialPointProcess allows n to be any positive integer and reg any parameter-free region.
- BinomialPointProcess can be used with such functions as RipleyK and RandomPointConfiguration.
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Examples
open allclose allBasic Examples (2)
Scope (2)
Sample from any valid RegionQ whose RegionEmbeddingDimension is equal to its RegionDimension:
Applications (1)
Properties & Relations (4)
The number of points in a BinomialPointProcess is defined by n:
Simulate BinomialPointProcess over a unit disk:
The corresponding PointCountDistribution over a bounded subset:
Compare the histogram of point counts in the subset with the PDF:
Fit a BinomialDistribution to the point counts:
Test against theoretical distribution:
PointCountDistribution over region covering:
Point count distribution for the covering:
Compute void probabilities for a binomial point process:
Binomial point process is stationary—the intensity is translation invariant:
Text
Wolfram Research (2020), BinomialPointProcess, Wolfram Language function, https://reference.wolfram.com/language/ref/BinomialPointProcess.html.
CMS
Wolfram Language. 2020. "BinomialPointProcess." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/BinomialPointProcess.html.
APA
Wolfram Language. (2020). BinomialPointProcess. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/BinomialPointProcess.html