Assembly: Extreme.Numerics (Extreme.Numerics)
Syntax
| Visual Basic (Declaration) |
|---|
Public MustInherit Class Distribution |
| C# |
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public abstract class Distribution |
| C++ |
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public ref class Distribution abstract |
Methods
| Icon | Type | Description |
|---|---|---|
 | Equals(Object) | |
 | Finalize() | |
| GetHashCode() | Serves as a hash function for a particular type. |
| GetType() | Gets the Type of the current instance. |
| MemberwiseClone() | Creates a shallow copy of the current Object. |
| ToString() |
Constructors
| Icon | Type | Description |
|---|---|---|
| DistributionNew() |
Constructs a new Distribution object.
|
Properties
| Icon | Type | 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.
|
| Variance |
Gets the variance of the distribution.
|
Remarks
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.
Inheritance Hierarchy
