ImageLevels[image] gives a list of pixel values and counts for each channel in image. ImageLevels[image, n] bins pixel values into n equally spaced levels.ImageLevels[image, ...

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 ...

Linear programming problems are optimization problems where the objective function and constraints are all linear. Mathematica has a collection of algorithms for solving ...

The functions described here are among the most commonly used continuous univariate statistical distributions. You can compute their densities, means, variances, and other ...

Sum[f, {i, i_max}] evaluates the sum \[Sum]i = 1 i_max f. Sum[f, {i, i_min, i_max}] starts with i = i_min. Sum[f, {i, i_min, i_max, di}] uses steps d i. Sum[f, {i, {i_1, i_2, ...

Reduce[expr, vars] reduces the statement expr by solving equations or inequalities for vars and eliminating quantifiers. Reduce[expr, vars, dom] does the reduction over the ...

ANOVA[data] performs a one-way analysis of variance.ANOVA[data, model, vars] performs an analysis of variance for model as a function of the categorical variables vars.

ArrayFlatten[{{m_11, m_12, ...}, {m_21, m_22, ...}, ...}] creates a single flattened matrix from a matrix of matrices m i j. ArrayFlatten[a, r] flattens out r pairs of levels ...

Minors[m] gives the minors of a matrix m. Minors[m, k] gives the k\[Null]\[Null]^th minors.

StateOutputEstimator[ss, l] constructs an estimator for the StateSpaceModel object ss with estimator gain matrix l.StateOutputEstimator[{ss, sensors}, l] uses only sensors as ...