Details

	Automatic	automatically choose the parameter estimator
	"MaximumLikelihood"	maximize the likelihood
	"MaximumPseudoLikelihood"	maximize the pseudo likelihood
	"FindClusters"	use FindClusters function
	"MethodOfMoments"	use a homogeinity measure to estimate the parameters

Settings for PointProcessEstimator are documented under the individual point process reference pages.
With the setting PointProcessEstimator->{"estimator",Method->{"solver",opts}}, additional options can be given for the solver.
Solver methods that do not rely on starting values will not make use of starting values given to EstimatedPointProcess or FindPointProcessParameters.

Examples

Simulate a point configuration from a Matérn point process:

Wolfram Language code:

proc = MaternPointProcess[20, 30, 1, 2];
points = RandomPointConfiguration[proc, Rectangle[]];

Wolfram Language code: ListPlot[points]

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

Wolfram Language code: est = EstimatedPointProcess[points, MaternPointProcess[a, b, c, d], PointProcessEstimator -> "FindClusters"]

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

Wolfram Language code:

DiscretePlot[{RipleyK[proc, r], RipleyK[est, r]}, {r, 0.1, .5, .005}, PlotLegends -> {"original process", "estimated model"}]

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