NormalDistribution (Built-in Mathematica Symbol)

NormalDistribution[\[Mu], \[Sigma]] represents a normal (Gaussian) distribution with mean \[Mu] and standard deviation \[Sigma].NormalDistribution[] represents a normal ...

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 ...

CDF (Built-in Mathematica Symbol)

CDF[dist, x] gives the cumulative distribution function for the symbolic distribution dist evaluated at x.CDF[dist, {x_1, x_2, ...}] gives the multivariate cumulative ...

GumbelDistribution (Built-in Mathematica Symbol)

GumbelDistribution[\[Alpha], \[Beta]] represents a Gumbel distribution with location parameter \[Alpha] and scale parameter \[Beta].

NoncentralFRatioDistribution (Built-in Mathematica Symbol)

NoncentralFRatioDistribution[n, m, \[Lambda]] represents a noncentral F-ratio distribution with n numerator degrees of freedom, m denominator degrees of freedom, and ...

SinghMaddalaDistribution (Built-in Mathematica Symbol)

SinghMaddalaDistribution[q, a, b] represents the Singh\[Dash]Maddala distribution with shape parameters q and a and scale parameter b.

Expectation (Built-in Mathematica Symbol)

Expectation[expr, x \[Distributed] dist] gives the expectation of expr under the assumption that x follows the probability distribution dist. Expectation[expr, x ...

InverseGaussianDistribution (Built-in Mathematica Symbol)

InverseGaussianDistribution[\[Mu], \[Lambda]] represents an inverse Gaussian distribution with mean \[Mu] and scale parameter \[Lambda].InverseGaussianDistribution[\[Mu], ...

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 ...

FindDistributionParameters (Built-in Mathematica Symbol)

FindDistributionParameters[data, dist] finds the parameter estimates for the distribution dist from data.FindDistributionParameters[data, dist, {{p, p_0}, {q, q_0}, ...}] ...