[SerializableAttribute]
public abstract class DiscreteDistribution : Distribution

<SerializableAttribute>
Public MustInherit Class DiscreteDistribution
	Inherits Distribution

[SerializableAttribute]
public ref class DiscreteDistribution abstract : public Distribution

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

The DiscreteDistribution type exposes the following members.

	Name	Description
	DiscreteDistribution	Constructs a new DiscreteDistribution object.

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

	Name	Description
	DistributionFunction	Gets the probability of obtaining an outcome less than or equal to a specified value.
	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.)
	GetExpectedHistogram(IndexIntervalInt32, Double)	Returns a histogram whose bins contain the expected number of samples from the distribution for a given total number of samples.
	GetExpectedHistogram(IndexInt32, Double)	Returns a histogram whose bins contain the expected number of samples from the distribution for a given total number of samples.
	GetExpectedHistogram(Int32, Int32, Double)	Returns a histogram whose bins contain the expected number of samples from the distribution for a given total number of samples.
	GetHashCode	Serves as a hash function for a particular type. (Inherited from Object.)
	GetRandomSequence	Returns a sequence of random samples from the distribution.
	GetRandomSequence(Random)	Returns a sequence of random samples from the distribution.
	GetRandomSequence(Random, Int32)	Returns a sequence of random samples of the specified length from the distribution.
	GetRandomVariate	Returns a random sample from the distribution.
	GetRandomVariates(Random, Int32)	Fills an Int32 array with random numbers.
	GetRandomVariates(Random, Int32, Int32, Int32)	Fills an Int32 array with random numbers from this DiscreteDistribution.
	GetType	Gets the Type of the current instance. (Inherited from Object.)
	InverseDistributionFunction	Returns the inverse of the distribution function.
	LeftTailProbability	Gets the probability of obtaining a sample that is less than or less than or equal to the specified upper bound.
	MemberwiseClone	Creates a shallow copy of the current Object. (Inherited from Object.)
	Probability(Int32)	Returns the probability of obtaining a specific integer value in the distribution.
	Probability(Int32, Int32)	Gets the probability of obtaining a sample that falls within the specified interval from the distribution.
	RightTailProbability	Gets the probability of obtaining a sample that is less than or less than or equal to the specified upper bound.
	Sample	Returns a random sample from the distribution.
	Sample(Int32)	Returns a vector of random samples from the distribution.
	Sample(Random)	Returns a random sample from the distribution.
	Sample(Int32, Random)	Returns a vector of random samples from the distribution.
	Sample(Random, Int32)	Fills an Int32 array with random numbers.
	Sample(Random, Int32, Int32, Int32)	Fills an Int32 array with random numbers from this DiscreteDistribution.
	ToString	Returns a string that represents the current object. (Inherited from Object.)
	TwoTailProbability	Gets the probability of obtaining a sample that is less than or less than or equal to the specified upper bound.

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

A discrete probability distribution is a statistical distribution whose variables can take on only discrete values.

This library contains classes for the most common discrete distributions. They are listed in the table below.

DiscreteDistribution is an abstract base class that cannot be instantiated. To create a continuous distribution of a specific type, instantiate a class derived from DiscreteDistribution.

Notes to inheritors: When you inherit from DiscreteDistribution, you must override the following members: Probability(Int32), DistributionFunction(Int32), Sample, Mean, and Variance.

Numerical Libraries

Reference