DirichletDistribution (Built-in Mathematica Symbol)

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

DiscreteUniformDistribution (Built-in Mathematica Symbol)

DiscreteUniformDistribution[{i_min, i_max}] represents a discrete uniform distribution over the integers from i_min to i_max.DiscreteUniformDistribution[{{i_min, i_max}, ...

ExampleData (Built-in Mathematica Symbol)

ExampleData["type"] gives a list of names of examples of the specified type.ExampleData[{" type", " name"}] gives the default form of the named example of the specified ...

GompertzMakehamDistribution (Built-in Mathematica Symbol)

GompertzMakehamDistribution[\[Lambda], \[Xi]] represents a Gompertz distribution with scale parameter \[Lambda] and frailty parameter ...

LogitModelFit (Built-in Mathematica Symbol)

LogitModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a binomial logistic regression model of the form 1/(1 + E -(\[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + \ ...)) ...

MaxStableDistribution (Built-in Mathematica Symbol)

MaxStableDistribution[\[Mu], \[Sigma], \[Xi]] represents a generalized maximum extreme value distribution with location parameter \[Mu], scale parameter \[Sigma], and shape ...

MinStableDistribution (Built-in Mathematica Symbol)

MinStableDistribution[\[Mu], \[Sigma], \[Xi]] represents a generalized minimum extreme value distribution with location parameter \[Mu], scale parameter \[Sigma], and shape ...

MultivariateTDistribution (Built-in Mathematica Symbol)

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

PERTDistribution (Built-in Mathematica Symbol)

PERTDistribution[{min, max}, c] represents a PERT distribution with range min to max and maximum at c.PERTDistribution[{min, max}, c, \[Lambda]] represents a modified PERT ...

ProbitModelFit (Built-in Mathematica Symbol)

ProbitModelFit[{y_1, y_2, ...}, {f_1, f_2, ...}, x] constructs a binomial probit regression model of the form 1/2 (1 + erf((\[Beta]_0 + \[Beta]_1 f_1 + \[Beta]_2 f_2 + \ ...