Introduction (CUDALink Tutorial)

CUDALink allows Mathematica to use the CUDA parallel computing architecture on Graphical Processing Units (GPUs). It contains functions that use CUDA-enabled GPUs to boost ...

NDSolve (Built-in Mathematica Symbol)

NDSolve[eqns, y, {x, x_min, x_max}] finds a numerical solution to the ordinary differential equations eqns for the function y with the independent variable x in the range ...

BreadthFirstScan (Built-in Mathematica Symbol)

BreadthFirstScan[g, s, {"event_1" -> f_1, "event_2" -> f_2, ...}] performs a breadth-first scan (bfs) of the graph g starting at the vertex s and evaluates f_i whenever ...

CellularAutomaton (Built-in Mathematica Symbol)

CellularAutomaton[rule, init, t] generates a list representing the evolution of the cellular automaton with the specified rule from initial condition init for t steps. ...

CopulaDistribution (Built-in Mathematica Symbol)

CopulaDistribution[ker, {dist_1, dist_2, ...}] represents a copula distribution with kernel distribution ker and marginal distributions dist_1, dist_2, ....

DistributionFitTest (Built-in Mathematica Symbol)

DistributionFitTest[data] tests whether data is normally distributed. DistributionFitTest[data, dist] tests whether data is distributed according to dist. ...

LocationTest (Built-in Mathematica Symbol)

LocationTest[data] tests whether the mean or median of the data is zero. LocationTest[{data_1, data_2}] tests whether the means or medians of data_1 and data_2 are ...

PolyhedronData (Built-in Mathematica Symbol)

PolyhedronData[poly, " property"] gives the value of the specified property for the polyhedron named poly.PolyhedronData[poly] gives an image of the polyhedron named ...

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. ...

SmoothKernelDistribution (Built-in Mathematica Symbol)

SmoothKernelDistribution[{x_1, x_2, ...}] represents a smooth kernel distribution based on the data values x_i.SmoothKernelDistribution[{{x_1, y_1, ...}, {x_2, y_2, ...}, ...