InverseGammaDistribution (Built-in Mathematica Symbol)

InverseGammaDistribution[\[Alpha], \[Beta]] represents an inverse gamma distribution with shape parameter \[Alpha] and scale parameter ...

InverseGaussianDistribution (Built-in Mathematica Symbol)

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

ListCorrelate (Built-in Mathematica Symbol)

ListCorrelate[ker, list] forms the correlation of the kernel ker with list. ListCorrelate[ker, list, k] forms the cyclic correlation in which the k \[Null]^th element of ker ...

Partition (Built-in Mathematica Symbol)

Partition[list, n] partitions list into non-overlapping sublists of length n. Partition[list, n, d] generates sublists with offset d. Partition[list, {n_1, n_2, ...}] ...

TimeValue (Built-in Mathematica Symbol)

TimeValue[s, i, t] calculates the time value of a security s at time t for an interest specified by i.

Loops and Control Structures (Mathematica Tutorial)

The execution of a Mathematica program involves the evaluation of a sequence of Mathematica expressions. In simple programs, the expressions to be evaluated may be separated ...

Composition and Splitting Methods for ... (Mathematica Tutorial)

In some cases it is useful to split the differential system into subsystems and solve each subsystem using appropriate integration methods. Recombining the individual ...

Gauss–Newton Methods (Mathematica Tutorial)

For minimization problems for which the objective function is a sum of squares, it is often advantageous to use the special structure of the problem. Time and effort can be ...

Eigensystem (Built-in Mathematica Symbol)

Eigensystem[m] gives a list {values, vectors} of the eigenvalues and eigenvectors of the square matrix m. Eigensystem[{m, a}] gives the generalized eigenvalues and ...

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, ...}, ...