NoncentralBetaDistribution (Built-in Mathematica Symbol)

NoncentralBetaDistribution[\[Alpha], \[Beta], \[Delta]] represents a noncentral beta distribution with shape parameters \[Alpha], \[Beta] and noncentrality parameter \[Delta].

NoncentralChiSquareDistribution (Built-in Mathematica Symbol)

NoncentralChiSquareDistribution[\[Nu], \[Lambda]] represents a noncentral \[Chi]^2 distribution with \[Nu] degrees of freedom and noncentrality parameter \[Lambda].

PascalDistribution (Built-in Mathematica Symbol)

PascalDistribution[n, p] represents a Pascal distribution with parameters n and p.

RotationTransform (Built-in Mathematica Symbol)

RotationTransform[\[Theta]] gives a TransformationFunction that represents a rotation in 2D by \[Theta] radians about the origin.RotationTransform[\[Theta], p] gives a 2D ...

SiegelTukeyTest (Built-in Mathematica Symbol)

SiegelTukeyTest[{data_1, data_2}] tests whether the variances of data_1 and data_2 are equal.SiegelTukeyTest[dspec, \[Sigma]_0^2] tests a dispersion measure against ...

SkewNormalDistribution (Built-in Mathematica Symbol)

SkewNormalDistribution[\[Mu], \[Sigma], \[Alpha]] represents a skew-normal distribution with shape parameter \[Alpha], location parameter \[Mu], and scale parameter \[Sigma].

TukeyLambdaDistribution (Built-in Mathematica Symbol)

TukeyLambdaDistribution[\[Lambda]] represents Tukey's lambda distribution with shape parameter \[Lambda].TukeyLambdaDistribution[\[Lambda], \[Mu], \[Sigma]] represents ...

Partitioning Data into Clusters (Mathematica Tutorial)

Cluster analysis is an unsupervised learning technique used for classification of data. Data elements are partitioned into groups called clusters that represent proximate ...

Quasi-Newton Methods (Mathematica Tutorial)

There are many variants of quasi-Newton methods. In all of them, the idea is to base the matrix B_k in the quadratic model on an approximation of the Hessian matrix built up ...

SurvivalDistribution (Built-in Mathematica Symbol)

SurvivalDistribution[{e_1, e_2, ...}] represents a survival distribution with event times e_i.SurvivalDistribution[{w_1, w_2, ...} -> {e_1, e_2, ...}] represents a survival ...