KuiperTest (Built-in Mathematica Symbol)

KuiperTest[data] tests whether data is normally distributed using the Kuiper test.KuiperTest[data, dist] tests whether data is distributed according to dist using the Kuiper ...

MultivariatePoissonDistribution (Built-in Mathematica Symbol)

MultivariatePoissonDistribution[\[Mu]_0, {\[Mu]_1, \[Mu]_2, \ ...}] represents a multivariate Poisson distribution with mean vector {\[Mu]_0 + \[Mu]_1, \[Mu]_0 + \[Mu]_2, ...

PlotRange (Built-in Mathematica Symbol)

PlotRange is an option for graphics functions that specifies what range of coordinates to include in a plot.

SmoothHistogram (Built-in Mathematica Symbol)

SmoothHistogram[{x_1, x_2, ...}] plots a smooth kernel histogram of the values x_i.SmoothHistogram[{x_1, x_2, ...}, espec] plots a smooth kernel histogram with estimator ...

SmoothKernelDistribution (Built-in Mathematica Symbol)

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

DistributionFitTest (Built-in Mathematica Symbol)

DistributionFitTest[data] tests whether data is normally distributed. DistributionFitTest[data, dist] tests whether data is distributed according to dist. ...

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].

ProbabilityScalePlot (Built-in Mathematica Symbol)

ProbabilityScalePlot[{x_1, x_2, ...}] generates a normal probability plot of the samples x_i. ProbabilityScalePlot[{x_1, x_2, ...}, " dist"] generates a probability plot ...

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, ...}, ...