generates a pseudorandom spatial point configuration from the spatial point process pproc in the observation region reg.

RandomPointConfiguration[pproc,reg, n]

generates an ensemble of n spatial point configurations.

Details and Options

  • RandomPointConfiguration takes a point process pproc and generates a point configuration as a SpatialPointData object.
  • RandomPointConfiguration gives a different realization of pseudorandom point configurations whenever you run the Wolfram Language. You can start with a particular seed using SeedRandom.
  • The same process can generate an ensemble consisting of different realizations.
  • The observation region reg needs to be a parameter-free region, as well as SpatialObservationRegionQ.
  • The following options can be given:
  • MethodAutomaticwhat method to use
    WorkingPrecisionMachinePrecisionprecision used in internal computations
  • With the setting WorkingPrecisionp, random numbers of precision p will be generated.
  • Special settings for Method are documented under the individual point process reference pages.
  • Typical Method settings include:
  • "MCMC"Markov chain Monte Carlo birth and death
    "Thinning"random thinning
    "Exact"coupling from the past


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Basic Examples  (2)

Sample from a Poisson point process:

Sample 5 realizations from a binomial point process:

Scope  (5)

RandomPointConfiguration returns a SpatialPointData object:

Obtain a list of locations of the points:

Simulate a Strauss point process in a rectangle:

Retrieve points that lie within a unit disk centered at {2,3}:

Visualize points on the plane:

Estimate the parameters for a point process using a simulated point configuration:

Simulate from a Cauchy point process:

Estimate Ripley's K function from the sampled point configuration and compare it with the theoretical K function:

Simulate an ensemble of 5 realizations over the same region:

Number of points in each realization:

Visualize the distribution of points in different realizations:

Options  (3)

Method  (2)

Sample from an InhomogeneousPoissonPointProcess using the different methods:

Use the method "Thinning":

Use the Markov chain Monte Carlo method "MCMC":

Visualize samples over the region:

Sample from a Gibbs point process using the Markov chain Monte Carlo method "MCMC" with the number of iterations equal to 30000:

WorkingPrecision  (1)

Generate a sample point configuration with default machine precision:

Use WorkingPrecision to generate a sample point configuration with higher precision:

Applications  (2)

Estimate the density of a PoissonPointProcess from a sample:

The intensity estimate:

Compare the expected point counts and the average of number of points for an inhomogeneous Poisson point process:

Wolfram Research (2020), RandomPointConfiguration, Wolfram Language function, https://reference.wolfram.com/language/ref/RandomPointConfiguration.html.


Wolfram Research (2020), RandomPointConfiguration, Wolfram Language function, https://reference.wolfram.com/language/ref/RandomPointConfiguration.html.


@misc{reference.wolfram_2020_randompointconfiguration, author="Wolfram Research", title="{RandomPointConfiguration}", year="2020", howpublished="\url{https://reference.wolfram.com/language/ref/RandomPointConfiguration.html}", note=[Accessed: 20-January-2021 ]}


@online{reference.wolfram_2020_randompointconfiguration, organization={Wolfram Research}, title={RandomPointConfiguration}, year={2020}, url={https://reference.wolfram.com/language/ref/RandomPointConfiguration.html}, note=[Accessed: 20-January-2021 ]}


Wolfram Language. 2020. "RandomPointConfiguration." Wolfram Language & System Documentation Center. Wolfram Research. https://reference.wolfram.com/language/ref/RandomPointConfiguration.html.


Wolfram Language. (2020). RandomPointConfiguration. Wolfram Language & System Documentation Center. Retrieved from https://reference.wolfram.com/language/ref/RandomPointConfiguration.html