[SerializableAttribute]
public class NegativeBinomialDistribution : DiscreteDistribution

<SerializableAttribute>
Public Class NegativeBinomialDistribution
	Inherits DiscreteDistribution

[SerializableAttribute]
public ref class NegativeBinomialDistribution : public DiscreteDistribution

[<SerializableAttribute>]
type NegativeBinomialDistribution =  
    class
        inherit DiscreteDistribution
    end

The NegativeBinomialDistribution type exposes the following members.

	Name	Description
	NegativeBinomialDistribution	Constructs a new NegativeBinomialDistribution object.

	Name	Description
	Kurtosis	Gets the kurtosis of the distribution. (Overrides DistributionKurtosis.)
	Mean	Gets the mean or expectation value of the distribution. (Overrides DistributionMean.)
	NumberOfTrials	Gets the number of trials.
	ProbabilityOfSuccess	Gets the probability that a trial is successful.
	Skewness	Gets the skewness of the distribution. (Overrides DistributionSkewness.)
	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. (Overrides DistributionVariance.)

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

The negative binomial distribution models the number of failures before a specified number of successes in a series of Bernoulli trials. A Bernoulli trial is an experiment with two possible outcomes, labeled 'success' and 'failure,' where the probability of success has a fixed value for all trials.

A negative binomial distribution has two parameters: the NumberOfTrials and the ProbabilityOfSuccess of an individual trial.

When playing a video game where the probability of completing a level is constant, the total number of levels completed before the three lives are used up has a negative binomial distribution.

Numerical Libraries

Reference