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BinomialPointProcess—Wolfram Language Documentation

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• See Also
  • PoissonPointProcess
  • InhomogeneousPoissonPointProcess
  • GibbsPointProcess
  • BinomialDistribution
  • MultinomialDistribution
  • SpatialPointData
• Related Guides
  • Spatial Point Processes
  • Spatial Statistics

• 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,…,ν_n/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.

A shooter at a range shoots 12 bullets at random at a round target of diameter 1 meter. Simulate possible bullet patterns:

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 goodness of fit:

Test against theoretical distribution:

PointCountDistribution over region covering:

Define a three-set covering:

Point count distribution for the covering:

Compute void probabilities for a binomial point process:

For a rectangle:

Binomial point process is stationary—the intensity is translation invariant:

Point count distribution in a subregion:

Point count distribution in the translated subregion:

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