KernelDensity Class

public static class KernelDensity

Public NotInheritable Class KernelDensity

public ref class KernelDensity abstract sealed

[<AbstractClassAttribute>]
[<SealedAttribute>]
type KernelDensity =  class end

The KernelDensity type exposes the following members.

	Name	Description
	Estimate(VectorDouble, Kernel, VectorDouble, Double, KernelDensityBandwidthEstimator, Double)	Estimates the density of the input
	Estimate(VectorDouble, Kernel, Double, Double, KernelDensityBandwidthEstimator, Double)	Estimates the density of the input
	Estimate(VectorDouble, Kernel, Int32, Double, Double, Double, Double, KernelDensityBandwidthEstimator, Double)	Estimates the density of the input.
	EstimateBandwidth	Estimates the bandwidth for kernel density estimation using the specified data and method.
	EstimateDistribution	Estimates the probability density of the input variable.
	NormalReferenceBandwidth	Returns the bandwidth for kernel density estimation based on Silverman's rule.
	ScottBandwidth	Returns the bandwidth for kernel density estimation based on Scott's rule.
	SilvermanBandwidth	Returns the bandwidth for kernel density estimation based on Silverman's rule.

	Name	Description
	BiweightKernel	Represents a bi-weight kernel.
	CosineKernel	Represents a cosine kernel.
	CosineKernel2	Represents an alternative cosine kernel.
	EpanechnikovKernel	Represents an Epanechnikov kernel.
	GaussianKernel	Represents a Gaussian kernel.
	LogisticKernel	Represents a logistic kernel.
	TriangularKernel	Represents a kernel shaped like a triangle.
	TricubicKernel	Represents a tri-cubic kernel.
	TriweightKernel	Represents a tri-weight kernel.
	UniformKernel	Represents a uniform kernel, shaped like a rectangle.

Use the methods in the KernelDensity class to perform kernel density estimation on a variable. Methods for estimating a suitable bandwidth are also available.

KernelDensity also defines several kernels, including the GaussianKernel, UniformKernel (flat top kernel), and EpanechnikovKernel.

Reference