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Meta.Numerics.Statistics.Distributions Namespace

Contains types that describe probability distributions.
Public classBernoulliDistribution
Represents a Bernoulli distribution.
Public classBernoulliFitResult
Represents the result of a fit to a Bernoulli distribution.
Public classBetaDistribution
Represents a beta distribution.
Public classBetaFitResult
Contains the result of a fit of a sample to a Beta distribution.
Public classBinomialDistribution
Represents a discrete binomial distribution.
Public classCauchyDistribution
Represents a Cauchy distribution.
Public classChiDistribution
Represents a χ distribution.
Public classChiSquaredDistribution
Represents a χ2 distribution.
Public classContinuousDistribution
Represents all continuous, univariate probability distribution.
Public classDiscreteDistribution
Represents all discrete, univariate probability distributions.
Public classDiscreteUniformDistribution
Describes a discrete uniform distribution.
Public classDistributionFitResultT
Represents the result of a fit to a distribution.
Public classExponentialDistribution
Represents an exponential distribution.
Public classExponentialFitResult
Represents the result of a fit to the exponential distribution.
Public classFisherDistribution
Represents the distribution of Fisher's F-statistic.
Public classFrechetDistribution
Represents a Fréchet distribution.
Public classGammaDistribution
Represents a Gamma distribution.
Public classGammaFitResult
Contains the result of a fit of a sample to a gamma distribution.
Public classGeometricDistribution
Represents a geometric distribution.
Public classGumbelDistribution
Represents a Gumbel distribution.
Public classGumbelFitResult
Represents the result of fitting sample data to a Gumbel distribution.
Public classHypergeometricDistribution
Represents a hypergeometric distribution.
Public classKolmogorovDistribution
Represents the distribution of the Kolmogorov-Smirnov D statistic.
Public classKuiperDistribution
Represents the asymptotic distribution of Kuiper's V statistic.
Public classLaplaceDistribution
Represents a Laplace distribution.
Public classLogisticDistribution
Represents a logistic distribution.
Public classLognormalDistribution
Represents a log-normal distribution.
Public classLognormalFitResult
Contains the result of a fit to a log-normal distribution.
Public classMomentMath
Contains methods for converting between different kinds of moments.
Public classNegativeBinomialDistribution
Represents a negative binomial distribution.
Public classNoncentralChiSquaredDistribution
Represents a non-central chi squared distribution.
Public classNormalDistribution
Represents a normal (Gaussian) distribution.
Public classNormalFitResult
Represents the result of a sample to a normal distribution.
Public classParetoDistribution
Represents a Pareto or power law distribution.
Public classPearsonRDistribution
Represents the distribution of Pearsons's r statistic.
Public classPoissonDistribution
Represented a Poisson distribution.
Public classRayleighDistribution
Represents a Rayleigh distribution.
Public classRayleighFitResult
Contains the result of a fit to a Rayleigh distribution.
Public classStudentDistribution
Represents the distribution of Student's t statistic.
Public classTriangularDistribution
Represents a triangular distribution.
Public classUniformDistribution
Represents a uniform distribution over an interval.
Public classUnivariateDistribution
Represents a probability distribution over a single variable.
Public classWaldDistribution
Represents a Wald (Inverse Gaussian) distribution.
Public classWaldFitResult
Contains the result of the fit of a sample to a Wald (Inverse Gaussian) distribution.
Public classWeibullDistribution
Represents a Weibull distribution.
Public classWeibullFitResult
Represents the result of a sample to a normal distribution.

Distributions are assignments of a probability-weight 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 pseduo-random 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 pseudo-random floating-point values distributed according to a continuous distribution (using GetRandomValue(Random)). Many continuous distributions are defined, including NormalDistribution, BetaDistribution, GammaDistribution, and WeibullDistribution.

All one-dimensional 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.

See Also