Distribution Class

[SerializableAttribute]
public abstract class Distribution

<SerializableAttribute>
Public MustInherit Class Distribution

[SerializableAttribute]
public ref class Distribution abstract

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

Inheritance

Object  →  Distribution

Derived

Extreme.Statistics.Distributions.ContinuousDistribution

Extreme.Statistics.Distributions.DiscreteDistribution

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.

Reference