# PascalDistribution

PascalDistribution[n,p]

represents a Pascal distribution with parameters n and p.

# Details • The probability for value in a Pascal distribution is for , and is zero otherwise.
• PascalDistribution[n,p] gives the distribution of the number of trials with nonzero success probability p before n successes occur.
• PascalDistribution allows n to be any positive integer and p any positive real number less than or equal to 1.
• PascalDistribution allows n and p to be dimensionless quantities. »
• PascalDistribution can be used with such functions as Mean, CDF, and RandomVariate.

# Background & Context

• PascalDistribution[n,p] represents a discrete statistical distribution defined for integer values and determined by an integer parameter n ( ) and a real parameter p ( ) that represent the number of successes of an experiment and its probability of success, respectively. The Pascal distribution has a probability density function (PDF) that is discrete and unimodal. The Pascal distribution is one of a number of distributions that fit under the heading of "negative binomial distributions," though it should not be confused with "the" negative binomial distribution (NegativeBinomialDistribution).
• The Pascal distribution is one of the earliest studied probability distributions, with its roots dating back to the work of Blaise Pascal in the 1670s. Classically, the Pascal distribution (when viewed as a specific case of the family of negative binomial distributions) can be realized as an urn model illustrating the number of draws of a marble from an urn required to procure n marbles of a certain color, given that the draws are mutually independent with p probability of success. Since its inception, the distribution has been realized as an integer-n case of , and so many of its applications (e.g. in accident statistics and telecommunications) arise because of its inclusion in the family of negative binomial distributions. Other applications of the distribution include the modeling of population statistics, psychological data, quality control, and queueing theory.
• RandomVariate can be used to give one or more machine- or arbitrary-precision (the latter via the WorkingPrecision option) pseudorandom variates from a Pascal distribution. Distributed[x,PascalDistribution[n,p]], written more concisely as xPascalDistribution[n,p], can be used to assert that a random variable x is distributed according to a Pascal distribution. Such an assertion can then be used in functions such as Probability, NProbability, Expectation, and NExpectation.
• The probability density and cumulative distribution functions may be given using PDF[PascalDistribution[n,p],x] and CDF[PascalDistribution[n,p],x], though one should note that there is no closed-form expression for its PDF. The mean, median, variance, raw moments, and central moments may be computed using Mean, Median, Variance, Moment, and CentralMoment, respectively. These quantities can be visualized using DiscretePlot.
• DistributionFitTest can be used to test if a given dataset is consistent with a Pascal distribution, EstimatedDistribution to estimate a Pascal parametric distribution from given data, and FindDistributionParameters to fit data to a Pascal distribution. ProbabilityPlot can be used to generate a plot of the CDF of given data against the CDF of a symbolic Pascal distribution and QuantilePlot to generate a plot of the quantiles of given data against the quantiles of a symbolic Pascal distribution.
• TransformedDistribution can be used to represent a transformed Pascal distribution, CensoredDistribution to represent the distribution of values censored between upper and lower values, and TruncatedDistribution to represent the distribution of values truncated between upper and lower values. CopulaDistribution can be used to build higher-dimensional distributions that contain a Pascal distribution, and ProductDistribution can be used to compute a joint distribution with independent component distributions involving Pascal distributions.
• PascalDistribution is related to a number of other statistical distributions. As mentioned above, it is a member of the family of negative binomial distributions and hence is qualitatively related to NegativeBinomialDistribution (also known as Pólya distribution for noninteger n). GeometricDistribution is a transformation (TransformedDistribution) of PascalDistribution in that the CDF of the variate u-1 is precisely that of whenever uPascalDistribution[1,p]. PascalDistribution is also a transformation of NegativeBinomialDistribution, while the PDF of PascalDistribution[n,p] converges to that of NormalDistribution[μ,σ] as n for μ and σ the mean (Mean) and standard deviation (StandardDeviation), respectively, of PascalDistribution[n,p]. PascalDistribution is also related to PoissonDistribution, PoissonConsulDistribution, BinomialDistribution, NegativeBinomialDistribution, MultinomialDistribution, and NegativeMultinomialDistribution.

# Examples

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## Basic Examples(3)

Probability mass function:

 In:= Out= In:= Out= In:= Out= Cumulative distribution function:

 In:= Out= In:= Out= In:= Out= Mean and variance:

 In:= Out= In:= Out= ## Properties & Relations(5)

Introduced in 2010
(8.0)
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Updated in 2016
(10.4)