MarginalDistribution (Built-in Mathematica Symbol)

MarginalDistribution[dist, k] represents a univariate marginal distribution of the k\[Null]^th coordinate from the multivariate distribution dist.MarginalDistribution[dist, ...

StableDistribution (Built-in Mathematica Symbol)

StableDistribution[type, \[Alpha], \[Beta], \[Mu], \[Sigma]] represents the stable distribution S_type with index of stability \[Alpha], skewness parameter \[Beta], location ...

NegativeMultinomialDistribution (Built-in Mathematica Symbol)

NegativeMultinomialDistribution[n, p] represents a negative multinomial distribution with parameter n and failure probability vector p.

KernelMixtureDistribution (Built-in Mathematica Symbol)

KernelMixtureDistribution[{x_1, x_2, ...}] represents a kernel mixture distribution based on the data values x_i.KernelMixtureDistribution[{{x_1, y_1, ...}, {x_2, y_2, ...}, ...

GeometricDistribution (Built-in Mathematica Symbol)

GeometricDistribution[p] represents a geometric distribution with probability parameter p.

Alphabetical Listing (Mathematica Guide)

Mathematica has over 3000 built-in functions and other objects, all based on a single unified framework, and all carefully designed to work together, both in simple ...

DirichletDistribution (Built-in Mathematica Symbol)

DirichletDistribution[{\[Alpha]_1, ..., \[Alpha] k +1}] represents a Dirichlet distribution of dimension k with shape parameters \[Alpha]_i.

MultinormalDistribution (Built-in Mathematica Symbol)

MultinormalDistribution[\[Mu], \[CapitalSigma]] represents a multivariate normal (Gaussian) distribution with mean vector \[Mu] and covariance matrix \[CapitalSigma].

MultivariateTDistribution (Built-in Mathematica Symbol)

MultivariateTDistribution[\[CapitalSigma], \[Nu]] represents the multivariate Student t distribution with scale matrix \[CapitalSigma] and degrees of freedom parameter ...

BinormalDistribution (Built-in Mathematica Symbol)

BinormalDistribution[{\[Mu]_1, \[Mu]_2}, {\[Sigma]_1, \[Sigma]_\ 2}, \[Rho]] represents a bivariate normal distribution with mean {\[Mu]_1, \[Mu]_2} and covariance matrix ...