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Formatted Output   (Mathematica Tutorial)
Ever since Version 3 of Mathematica, there has been rich support for arbitrary mathematical typesetting and layout. Underlying all that power was a so-called box language, ...
ListConvolve   (Built-in Mathematica Symbol)
ListConvolve[ker, list] forms the convolution of the kernel ker with list. ListConvolve[ker, list, k] forms the cyclic convolution in which the k\[Null]^th element of ker is ...
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, ...
Plot3D   (Built-in Mathematica Symbol)
Plot3D[f, {x, x_min, x_max}, {y, y_min, y_max}] generates a three-dimensional plot of f as a function of x and y. Plot3D[{f_1, f_2, ...}, {x, x_min, x_max}, {y, y_min, ...
TTest   (Built-in Mathematica Symbol)
TTest[data] tests whether the mean of data is zero. TTest[{data_1, data_2}] tests whether the means of data_1 and data_2 are equal.TTest[dspec, \[Mu]_0] tests the mean ...
WaveletBestBasis   (Built-in Mathematica Symbol)
WaveletBestBasis[dwd] computes a best basis representation in the DiscreteWaveletData object dwd.WaveletBestBasis[dwd, cspec] computes a best basis representation using the ...
DiscreteMath`GraphPlot`   (Mathematica Compatibility Information)
GraphPlot functionality is now available in the newly added built-in Mathematica kernel functions GraphPlot and LayeredGraphPlot. GraphPlot3D is now available as the newly ...
ReverseBiorthogonalSplineWavelet   (Built-in Mathematica Symbol)
ReverseBiorthogonalSplineWavelet[] represents a reverse biorthogonal spline wavelet of order 4 and dual order 2.ReverseBiorthogonalSplineWavelet[n, m] represents a reverse ...
RootLocusPlot   (Built-in Mathematica Symbol)
RootLocusPlot[g, {k, k_min, k_max}] generates the root locus plot of a rational function g of k ranging from k_min to k_max.RootLocusPlot[sys, ...] plots the root loci of a ...
Random Number Generation   (Mathematica Tutorial)
The ability to generate pseudorandom numbers is important for simulating events, estimating probabilities and other quantities, making randomized assignments or selections, ...
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