Meta.Numerics.Statistics.Distributions Namespace 
Class  Description  

BernoulliDistribution 
Represents a Bernoulli distribution.
 
BernoulliFitResult 
Represents the result of a fit to a Bernoulli distribution.
 
BetaDistribution 
Represents a beta distribution.
 
BetaFitResult 
Contains the result of a fit of a sample to a Beta distribution.
 
BinomialDistribution 
Represents a discrete binomial distribution.
 
CauchyDistribution 
Represents a Cauchy distribution.
 
ChiDistribution 
Represents a χ distribution.
 
ChiSquaredDistribution 
Represents a χ^{2} distribution.
 
ContinuousDistribution 
Represents all continuous, univariate probability distribution.
 
DiscreteDistribution 
Represents all discrete, univariate probability distributions.
 
DiscreteUniformDistribution 
Describes a discrete uniform distribution.
 
DistributionFitResultT 
Represents the result of a fit to a distribution.
 
ExponentialDistribution 
Represents an exponential distribution.
 
ExponentialFitResult 
Represents the result of a fit to the exponential distribution.
 
FisherDistribution 
Represents the distribution of Fisher's Fstatistic.
 
FrechetDistribution 
Represents a Fréchet distribution.
 
GammaDistribution 
Represents a Gamma distribution.
 
GammaFitResult 
Contains the result of a fit of a sample to a gamma distribution.
 
GeometricDistribution 
Represents a geometric distribution.
 
GumbelDistribution 
Represents a Gumbel distribution.
 
GumbelFitResult 
Represents the result of fitting sample data to a Gumbel distribution.
 
HypergeometricDistribution 
Represents a hypergeometric distribution.
 
KolmogorovDistribution 
Represents the distribution of the KolmogorovSmirnov D statistic.
 
KuiperDistribution 
Represents the asymptotic distribution of Kuiper's V statistic.
 
LaplaceDistribution 
Represents a Laplace distribution.
 
LogisticDistribution 
Represents a logistic distribution.
 
LognormalDistribution 
Represents a lognormal distribution.
 
LognormalFitResult 
Contains the result of a fit to a lognormal distribution.
 
MomentMath 
Contains methods for converting between different kinds of moments.
 
NegativeBinomialDistribution 
Represents a negative binomial distribution.
 
NoncentralChiSquaredDistribution 
Represents a noncentral chi squared distribution.
 
NormalDistribution 
Represents a normal (Gaussian) distribution.
 
NormalFitResult 
Represents the result of a sample to a normal distribution.
 
ParetoDistribution 
Represents a Pareto or power law distribution.
 
PearsonRDistribution 
Represents the distribution of Pearsons's r statistic.
 
PoissonDistribution 
Represented a Poisson distribution.
 
RayleighDistribution 
Represents a Rayleigh distribution.
 
RayleighFitResult 
Contains the result of a fit to a Rayleigh distribution.
 
StudentDistribution 
Represents the distribution of Student's t statistic.
 
TriangularDistribution 
Represents a triangular distribution.
 
UniformDistribution 
Represents a uniform distribution over an interval.
 
UnivariateDistribution 
Represents a probability distribution over a single variable.
 
WaldDistribution 
Represents a Wald (Inverse Gaussian) distribution.
 
WaldFitResult 
Contains the result of the fit of a sample to a Wald (Inverse Gaussian) distribution.
 
WeibullDistribution 
Represents a Weibull distribution.
 
WeibullFitResult 
Represents the result of a sample to a normal distribution.

Distributions are assignments of a probabilityweight to each of the elements in a set. Most commonly, those sets are subsets of the integers or real numbers.
Distribution on the integers inherit from the abstract DiscreteDistribution class. For any discrete distribution, you can determine its range (called Support), the probability weight of each value (using ProbabilityMass(Int32)), and many other properties. You can generate pseduorandom integers distributed according to a discrete distribution (using GetRandomValue(Random)). Many discrete distributions are defined, including PoissonDistribution and BinomialDistribution.
Distributions on the real numbers inherit from the abstract ContinuousDistribution class. For any continuous distribution, you can determine its range (called Support), the probability density at each value (using ProbabilityDensity(Double)), the cumulative distribution function (using LeftProbability(Double)), and many other properties. You can generate pseudorandom floatingpoint values distributed according to a continuous distribution (using GetRandomValue(Random)). Many continuous distributions are defined, including NormalDistribution, BetaDistribution, GammaDistribution, and WeibullDistribution.
All onedimensional distibutions, continuous and discrete, inherit from the abstract UnivariateDistribution class. Using the properties and methods of this class, you can determine raw moments (RawMoment(Int32)) such as the Mean, central moments (CentralMoment(Int32)) such as the Variance, or cumulants (Cumulant(Int32)).
Many distributions also offer methods that allow you to find the parameters that best fit a given set of data points and measure the quality of the fit.
You can add your own continous and discrete distributions by inheriting from ContinuousDistribution or DiscreteDistribution and implementing only a few abstract methods. All the remaining properties and methods are then automatically determined for your distribution.