## Types in MathNet.Numerics.Statistics

Type KernelDensity

Namespace MathNet.Numerics.Statistics

Kernel density estimation (KDE).

### Public Static Functions

#### doubleEpanechnikovKernel(double x)

Epanechnikov Kernel: x => Math.Abs(x) <= 1.0 ? 3.0/4.0(1.0-x^2) : 0.0

#### doubleEstimate(double x, double bandwidth, IList<double> samples, Func<double, double> kernel)

Estimate the probability density function of a random variable.
The routine assumes that the provided kernel is well defined, i.e. a real non-negative function that integrates to 1.

#### doubleEstimateEpanechnikov(double x, double bandwidth, IList<double> samples)

Estimate the probability density function of a random variable with an Epanechnikov kernel. The Epanechnikov kernel is optimal in a mean square error sense.

#### doubleEstimateGaussian(double x, double bandwidth, IList<double> samples)

Estimate the probability density function of a random variable with a Gaussian kernel.

#### doubleEstimateTriangular(double x, double bandwidth, IList<double> samples)

Estimate the probability density function of a random variable with a triangular kernel.

#### doubleEstimateUniform(double x, double bandwidth, IList<double> samples)

Estimate the probability density function of a random variable with a uniform kernel.

#### doubleGaussianKernel(double x)

A Gaussian kernel (PDF of Normal distribution with mean 0 and variance 1). This kernel is the default.

#### doubleTriangularKernel(double x)

Triangular Kernel: x => Math.Abs(x) <= 1.0 ? (1.0-Math.Abs(x)) : 0.0

#### doubleUniformKernel(double x)

Uniform Kernel: x => Math.Abs(x) <= 1.0 ? 1.0/2.0 : 0.0