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EvaluationMonitor   (Built-in Mathematica Symbol)
EvaluationMonitor is an option for various numerical computation and plotting functions that gives an expression to evaluate whenever functions derived from the input are ...
InverseChiSquareDistribution   (Built-in Mathematica Symbol)
InverseChiSquareDistribution[\[Nu]] represents an inverse \[Chi]^2 distribution with \[Nu] degrees of freedom.InverseChiSquareDistribution[\[Nu], \[Xi]] represents a scaled ...
Linear Systems of ODEs   (Mathematica Tutorial)
Here is a system of two ODEs whose coefficient matrix has real and distinct eigenvalues. This solves the system. Note that the general solution depends on two arbitrary ...
Getting Started   (Parallel Package Tutorial)
Mathematica comes with all the tools and configuration so you can immediately carry out parallel computing. Note that, to take advantage of parallel computing it is often ...
Mathematica works with both exact quantities and approximate numbers. Using N , you can obtain a numerical approximation to an exact quantity with any desired precision or ...
Deleting Data with Raw SQL   (DatabaseLink Tutorial)
The raw SQL command DELETE deletes data from a database. An alternative is to use the Mathematica command SQLDelete, described in "Deleting Data". If you find that the ...
Nonlinear Conjugate Gradient Methods   (Mathematica Tutorial)
The basis for a nonlinear conjugate gradient method is to effectively apply the linear conjugate gradient method, where the residual is replaced by the gradient. A model ...
BetaRegularized   (Built-in Mathematica Symbol)
BetaRegularized[z, a, b] gives the regularized incomplete beta function I_z (a, b).
BetaBinomialDistribution   (Built-in Mathematica Symbol)
BetaBinomialDistribution[\[Alpha], \[Beta], n] represents a beta binomial mixture distribution with beta distribution parameters \[Alpha] and \[Beta], and n binomial trials.
NegativeMultinomialDistribution   (Built-in Mathematica Symbol)
NegativeMultinomialDistribution[n, p] represents a negative multinomial distribution with parameter n and failure probability vector p.
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