MultivariateContinuousDistribution Class

Abstract class for Multivariate Probability Distributions.

Inheritance Hierarchy

SystemObject
  Accord.Statistics.DistributionsDistributionBase
    Accord.Statistics.Distributions.MultivariateMultivariateContinuousDistribution
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Namespace: Accord.Statistics.Distributions.Multivariate
Assembly: Accord.Statistics (in Accord.Statistics.dll) Version: 3.8.0

Syntax

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[SerializableAttribute]
public abstract class MultivariateContinuousDistribution : DistributionBase, 
	IMultivariateDistribution, IDistribution, ICloneable, IMultivariateDistribution<double[]>, 
	IDistribution<double[]>, IFittableDistribution<double[]>, IFittable<double[]>, 
	ISampleableDistribution<double[]>, IRandomNumberGenerator<double[]>, IFormattable

<SerializableAttribute>
Public MustInherit Class MultivariateContinuousDistribution
	Inherits DistributionBase
	Implements IMultivariateDistribution, IDistribution, ICloneable, IMultivariateDistribution(Of Double()), 
	IDistribution(Of Double()), IFittableDistribution(Of Double()), 
	IFittable(Of Double()), ISampleableDistribution(Of Double()), 
	IRandomNumberGenerator(Of Double()), IFormattable

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The MultivariateContinuousDistribution type exposes the following members.

Constructors

	Name	Description
	MultivariateContinuousDistribution	Constructs a new MultivariateDistribution class.

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Properties

	Name	Description
	Covariance	Gets the variance-covariance matrix for this distribution.
	Dimension	Gets the number of variables for this distribution.
	Mean	Gets the mean for this distribution.
	Median	Gets the median for this distribution.
	Mode	Gets the mode for this distribution.
	Variance	Gets the variance for this distribution.

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Methods

	Name	Description
	Clone	Creates a new object that is a copy of the current instance. (Inherited from DistributionBase.)
	ComplementaryDistributionFunction	Gets the complementary cumulative distribution function (ccdf) for this distribution evaluated at point x. This function is also known as the Survival function.
	DistributionFunction	Gets the probability density function (pdf) for this distribution evaluated at point x.
	Equals	Determines whether the specified object is equal to the current object. (Inherited from Object.)
	Finalize	Allows an object to try to free resources and perform other cleanup operations before it is reclaimed by garbage collection. (Inherited from Object.)
	Fit(Double)	Fits the underlying distribution to a given set of observations.
	Fit(Double, IFittingOptions)	Fits the underlying distribution to a given set of observations.
	Fit(Double, Double)	Fits the underlying distribution to a given set of observations.
	Fit(Double, Int32)	Fits the underlying distribution to a given set of observations.
	Fit(Double, Double, IFittingOptions)	Fits the underlying distribution to a given set of observations.
	Fit(Double, Int32, IFittingOptions)	Fits the underlying distribution to a given set of observations.
	Generate	Generates a random observation from the current distribution.
	Generate(Double)	Generates a random observation from the current distribution.
	Generate(Int32)	Generates a random vector of observations from the current distribution.
	Generate(Int32)	Generates a random observation from the current distribution.
	Generate(Random)	Generates a random observation from the current distribution.
	Generate(Double, Random)	Generates a random observation from the current distribution.
	Generate(Int32, Double)	Generates a random vector of observations from the current distribution.
	Generate(Int32, Int32)	Generates a random vector of observations from the current distribution.
	Generate(Int32, Random)	Generates a random vector of observations from the current distribution.
	Generate(Int32, Random)	Generates a random observation from the current distribution.
	Generate(Int32, Double, Random)	Generates a random vector of observations from the current distribution.
	Generate(Int32, Int32, Random)	Generates a random vector of observations from the current distribution.
	GetHashCode	Serves as the default hash function. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	InnerComplementaryDistributionFunction	Gets the complementary cumulative distribution function (ccdf) for this distribution evaluated at point x. This function is also known as the Survival function.
	InnerDistributionFunction	Gets the probability density function (pdf) for this distribution evaluated at point x.
	InnerLogProbabilityDensityFunction	Gets the log-probability density function (pdf) for this distribution evaluated at point x.
	InnerProbabilityDensityFunction	Gets the probability density function (pdf) for this distribution evaluated at point x.
	LogProbabilityDensityFunction	Gets the log-probability density function (pdf) for this distribution evaluated at point x.
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	ProbabilityDensityFunction	Gets the probability density function (pdf) for this distribution evaluated at point x.
	ToString	Returns a String that represents this instance. (Inherited from DistributionBase.)
	ToString(IFormatProvider)	Returns a String that represents this instance. (Inherited from DistributionBase.)
	ToString(String)	Returns a String that represents this instance. (Inherited from DistributionBase.)
	ToString(String, IFormatProvider)	Returns a String that represents this instance. (Inherited from DistributionBase.)

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Extension Methods

	Name	Description
	HasMethod	Checks whether an object implements a method with the given name. (Defined by ExtensionMethods.)
	IsEqual	Compares two objects for equality, performing an elementwise comparison if the elements are vectors or matrices. (Defined by Matrix.)
	To(Type)	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)
	ToT	Overloaded. Converts an object into another type, irrespective of whether the conversion can be done at compile time or not. This can be used to convert generic types to numeric types during runtime. (Defined by ExtensionMethods.)

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Remarks

A probability distribution identifies either the probability of each value of an unidentified random variable (when the variable is discrete), or the probability of the value falling within a particular interval (when the variable is continuous).

The probability distribution describes the range of possible values that a random variable can attain and the probability that the value of the random variable is within any (measurable) subset of that range.

The function describing the probability that a given value will occur is called the probability function (or probability density function, abbreviated PDF), and the function describing the cumulative probability that a given value or any value smaller than it will occur is called the distribution function (or cumulative distribution function, abbreviated CDF).

References:

Reference

Accord.Statistics.Distributions.Multivariate Namespace

Inheritance Hierarchy