Computational Geometry Package (Computational Geometry Package Tutorial)

Computational geometry is the study of efficient algorithms for solving geometric problems. The nearest neighbor problem involves identifying one point, out of a set of ...

NProbability (Built-in Mathematica Symbol)

NProbability[pred, x \[Distributed] dist] gives the numerical probability for an event that satisfies the predicate pred under the assumption that x follows the probability ...

ExponentialDistribution (Built-in Mathematica Symbol)

ExponentialDistribution[\[Lambda]] represents an exponential distribution with scale inversely proportional to parameter \[Lambda].

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, ...}, ...

KirchhoffGraph (Built-in Mathematica Symbol)

KirchhoffGraph[kmat] gives the graph with Kirchhoff matrix kmat.KirchhoffGraph[{v_1, v_2, ...}, kmat] gives the graph with vertices v_i and Kirchhoff matrix kmat.

NIntegrate (Built-in Mathematica Symbol)

NIntegrate[f, {x, x_min, x_max}] gives a numerical approximation to the integral \[Integral]_x_min^x_max\ f\ d \ x. NIntegrate[f, {x, x_min, x_max}, {y, y_min, y_max}, ...] ...

PairedHistogram (Built-in Mathematica Symbol)

PairedHistogram[{x_1, x_2, ...}, {y_1, y_2, ...}] plots a paired histogram of the values x_i and y_i.PairedHistogram[{x_1, x_2, ...}, {y_1, y_2, ...}, bspec] plots a paired ...

Affymetrix (Mathematica Import/Export Format)

Affymetrix microarray data formats. Family of file formats used for the storage and exchange of microarray data and meta-information. ASCII or binary formats. Native formats ...

BernoulliDistribution (Built-in Mathematica Symbol)

BernoulliDistribution[p] represents a Bernoulli distribution with probability parameter p.

DesignMatrix (Built-in Mathematica Symbol)

DesignMatrix[{{x_11, x_12, ..., y_1}, {x_21, x_22, ..., y_2}, ...}, {f_1, f_2, ...}, {x_1, x_2, ...}] constructs the design matrix for the linear model \[Beta]_0 + \[Beta]_1 ...