[SerializableAttribute]
public abstract class Distribution

<SerializableAttribute>
Public MustInherit Class Distribution

[SerializableAttribute]
public ref class Distribution abstract

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

The Distribution type exposes the following members.

	Name	Description
	Distribution	Constructs a new Distribution object.

	Name	Description
	Kurtosis	Gets the kurtosis of the distribution.
	Mean	Gets the mean or expectation value of the distribution.
	Skewness	Gets the skewness of the distribution.
	StandardDeviation	Gets the standard deviation of the distribution.
	StatisticSymbol	Gets the common symbol to describe a statistic from the distribution.
	Variance	Gets the variance of the distribution.

	Name	Description
	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.)
	GetHashCode	Serves as a hash function for a particular type. (Inherited from Object.)
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	ToString	Returns a string that represents the current object. (Inherited from Object.)

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 or probability density function, and the function describing the cumulative probability that a given value or any value smaller than it will occur is called the cumulative distribution function.

Distributions can be univariate, meaning the outcome is expressed by a single number, or multivariate, meaning the outcome is expressed using multiple numbers. Most commonly used distributions are univariate distributions.

There are two main types of univariate distributions: discrete and continuous. A discrete probability distribution is a statistical distribution whose variables can take on only discrete values. A continuous probability distribution is a statistical distribution whose variables can take on any value within an interval. The interval can be infinite.

Notes to inheritors: You should not inherit from Distribution directly. Instead, inherit from DiscreteDistribution if you are implementing a discrete probability distribution, and from ContinuousDistribution if you are implementing a continuous probability distribution.

Numerical Libraries

Reference