EstimatedPointProcess

EstimatedPointProcess[pdata,pproc]

estimates the parametric point process pproc from point data pdata.

EstimatedPointProcess[pdata,pproc,{{p,p0},{q,q0},}]

estimates the parameters p, q, with starting values p0, q0, .

Details and Options

  • EstimatedPointProcess takes point data pdata and returns the symbolic point process pproc with parameter estimates inserted for any non-numeric values.
  •     
  • In general, a process pproc can be better estimated from an ensemble of point data.
  •     
  • The points pdata can have the following forms:
  • {p1,p2,}points pi
    GeoPosition[],GeoPositionXYZ[],geographic points
    SpatialPointData[]spatial point collection
    {pts,reg}point collection pts and observation region reg
  • The points are converted to a SpatialPointData object and a RipleyRasson estimator is used to generate the observation region if it is not provided in pdata.
  • The following options can be given:
  • AccuracyGoalAutomaticthe accuracy sought
    PointProcessEstimatorAutomaticwhat process parameter estimator to use
    PrecisionGoalAutomaticthe precision sought
    WorkingPrecisionAutomaticthe precision used in internal computations
  • Settings for PointProcessEstimator are documented under the individual point process reference pages.

Examples

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Basic Examples  (1)

Estimate the parameter of a PoissonPointProcess:

Compare the nearest neighbor function of the estimated process to the original data:

Scope  (3)

Cluster Point Processes  (1)

Simulate a point configuration from a Matern point process:

Use the "FindClusters" method to estimate a point process model:

Compare the Ripley measure between the original process and the estimated model:

Gibbs Point Processes  (2)

Estimate a hardcore point process:

Use automatic method:

Estimate an interaction point process:

Estimate the point process:

Options  (3)

PointProcessEstimator  (2)

Estimate a cluster point process:

Use the "FindClusters" method to estimate a point process model:

Use the method of moments:

Estimate an interaction process:

Use "MaximumPseudoLikelihood" method:

Use "MaximumLikelihood" method:

WorkingPrecision  (1)

Estimate a cluster point process with arbitrary precision:

Specify WorkingPrecision:

EstimatedPointProcess uses MachinePrecision as default:

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

Text

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

BibTeX

@misc{reference.wolfram_2020_estimatedpointprocess, author="Wolfram Research", title="{EstimatedPointProcess}", year="2020", howpublished="\url{https://reference.wolfram.com/language/ref/EstimatedPointProcess.html}", note=[Accessed: 22-April-2021 ]}

BibLaTeX

@online{reference.wolfram_2020_estimatedpointprocess, organization={Wolfram Research}, title={EstimatedPointProcess}, year={2020}, url={https://reference.wolfram.com/language/ref/EstimatedPointProcess.html}, note=[Accessed: 22-April-2021 ]}

CMS

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

APA

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