MultinormalDistribution (Built-in Mathematica Symbol)

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

RecurrenceTable (Built-in Mathematica Symbol)

RecurrenceTable[eqns, expr, {n, n_max}] generates a list of values of expr for successive n based on solving the recurrence equations eqns.RecurrenceTable[eqns, expr, nspec] ...

VectorPlot3D (Built-in Mathematica Symbol)

VectorPlot3D[{v_x, v_y, v_z}, {x, x_min, x_max}, {y, y_min, y_max}, {z, z_min, z_max}] generates a 3D vector plot of the vector field {v_x, v_y, v_z} as a function of x, y, ...

Writing Java Programs That Use ... (JLink Tutorial)

The first part of this User Guide describes using J/Link to allow you to call from Mathematica into Java, thereby extending the Mathematica environment to include the ...

ListVectorPlot3D (Built-in Mathematica Symbol)

ListVectorPlot3D[array] generates a 3D vector plot from a 3D array of vector field values.ListVectorPlot3D[{{{x_1, y_1, z_1}, {vx_1, vy_1, vz_1}}, ...}] generates a 3D vector ...

MultivariateTDistribution (Built-in Mathematica Symbol)

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

Parallelize (Built-in Mathematica Symbol)

Parallelize[expr] evaluates expr using automatic parallelization.

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 ...

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, ...

ProductDistribution (Built-in Mathematica Symbol)

ProductDistribution[dist_1, dist_2, ...] represents the joint distribution with independent component distributions dist_1, dist_2, ....