Represents a statistical distribution.
SystemObject Extreme.Statistics.DistributionsDistribution Extreme.Statistics.DistributionsContinuousDistribution Extreme.Statistics.DistributionsDiscreteDistribution
Namespace:
Extreme.Statistics.Distributions
Assembly:
Extreme.Numerics (in Extreme.Numerics.dll) Version: 8.1.1
[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.
|
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| Name | Description |
---|
 | Entropy |
Gets the entropy of the distribution.
|
 | 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.
|
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| 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 the default hash function. (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.) |
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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