MultivariateContinuousDistribution Class

[SerializableAttribute]
public abstract class MultivariateContinuousDistribution

<SerializableAttribute>
Public MustInherit Class MultivariateContinuousDistribution

[SerializableAttribute]
public ref class MultivariateContinuousDistribution abstract

[<AbstractClassAttribute>]
[<SerializableAttribute>]
type MultivariateContinuousDistribution = class end

Inheritance

Object  →  MultivariateContinuousDistribution

Derived

Extreme.Statistics.Distributions.DirichletDistribution

Extreme.Statistics.Distributions.GaussianMixtureDistribution

Extreme.Statistics.Distributions.MultivariateNormalDistribution

The distribution of a variable is a description of the relative numbers of times each possible outcome will occur in a number of trials. The function describing the distribution is called the probability function, and the function describing the cumulative probability that a given value or any value smaller than it will occur is called the distribution function.

A continuous probability distribution is a statistical distribution whose variables can take on any value within a certain interval. This interval may be infinite.

This library contains classes for the most common multivariate continuous distributions. They are listed in the table below:

MultivariateContinuousDistribution is an abstract base class that cannot be instantiated. To create a continuous distribution of a specific type, instantiate a class derived from MultivariateContinuousDistribution.

Notes to inheritors: When you inherit from MultivariateContinuousDistribution, you must override the following members: LogProbabilityDensityFunction(Vector<Double>), FillSampleCore(Random, Vector<Double>), GetMeans(), and GetVarianceCovarianceMatrix().

Reference