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, ...}, ...
Product
(Built-in Mathematica Symbol) Product[f, {i, i_max}] evaluates the product \[Product]i = 1 i_max f. Product[f, {i, i_min, i_max}] starts with i = i_min. Product[f, {i, i_min, i_max, di}] uses steps di. ...
Sum
(Built-in Mathematica Symbol) 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, ...
Linear programming problems are optimization problems where the objective function and constraints are all linear. Mathematica has a collection of algorithms for solving ...
GraphData[name] gives a graph with the specified name.GraphData[name, " property"] gives the value for the specified property for a named graph.GraphData["class"] gives a ...
Mathematica is uniquely suited for processing symbolic expressions because of its powerful pattern-matching abilities and large collection of built-in structural manipulation ...
A Diophantine polynomial system is an expression constructed with polynomial equations and inequalities combined using logical connectives and quantifiers where the variables ...
The NumericalDifferentialEquationAnalysis package combines functionality for analyzing differential equations using Butcher trees, Gaussian quadrature, and Newton-Cotes ...
SectorChart3D[{{x_1, y_1, z_1}, {x_2, y_2, z_2}, ...}] makes a 3D sector chart with sector angle proportional to x_i, radius y_i, and height z_i.SectorChart3D[{..., w_i[{x_i, ...
Solve
(Built-in Mathematica Symbol) Solve[expr, vars] attempts to solve the system expr of equations or inequalities for the variables vars. Solve[expr, vars, dom] solves over the domain dom. Common choices of ...