Represents the negative binomial distribution.

Namespace: Extreme.Statistics.Distributions
Assembly: Extreme.Numerics (Extreme.Numerics)

Syntax

Visual Basic (Declaration)
Public Class NegativeBinomialDistribution _ Inherits DiscreteDistribution

C#
public class NegativeBinomialDistribution : DiscreteDistribution

C++
public ref class NegativeBinomialDistribution : public DiscreteDistribution

Methods

Icon	Type	Description
	DistributionFunction(Int32)	Evaluates the cumulative distribution function of the distribution.
	Equals(Object)	Determines whether the specified Object is equal to the current Object.
	Finalize()	Allows an Object to attempt to free resources and perform other cleanup operations before the Object is reclaimed by garbage collection.
	GetExpectedHistogram(Int32, Int32, Double)	Gets a Histogram whose bins contain the expected number of samples for a given total number of samples.
	GetHashCode()	Serves as a hash function for a particular type.
	GetRandomVariate(Random, Int32, Double)	Returns a single random variate from a negative binomial distribution with the specified parameters.
	GetRandomVariate(Random)	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.
	MemberwiseClone()	Creates a shallow copy of the current Object.
	Probability(Int32)	Evaluates the probability function of the distribution.
	Probability(Int32, Int32)	Gets the probability of obtaining a sample that falls within the specified interval from the distribution.
	ToString()	Returns a String that represents the current Object.

Constructors

Icon	Type	Description
	NegativeBinomialDistributionNew(Int32, Double)	Constructs a new NegativeBinomialDistribution object.

Properties

Icon	Type	Description
	Kurtosis	Gets the kurtosis of the distribution.
	Mean	Gets the mean or expectation value of the distribution.
	NumberOfTrials	Gets the number of trials.
	ProbabilityOfSuccess	Gets the probability that a trial is successful.
	Skewness	Gets the skewness of the distribution.
	StandardDeviation	Gets the standard deviation of the distribution.
	Variance	Gets the variance of the distribution.

Remarks

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.

Examples

In jury selection, the total number of candidates before 12 jurors have been selected has a negative binomial distribution.

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.

Inheritance Hierarchy

System.Object
  Extreme.Statistics.Distributions.Distribution
    Extreme.Statistics.Distributions.DiscreteDistribution
      Extreme.Statistics.Distributions.NegativeBinomialDistribution