ListContourPlot (Built-in Mathematica Symbol)

ListContourPlot[array] generates a contour plot from an array of height values. ListContourPlot[{{x_1, y_1, f_1}, {x_2, y_2, f_2}, ...}] generates a contour plot from values ...

ListDensityPlot (Built-in Mathematica Symbol)

ListDensityPlot[array] generates a smooth density plot from an array of values. ListDensityPlot[{{x_1, y_1, f_1}, {x_2, y_2, f_2}, ...}] generates a density plot with values ...

ListLogPlot (Built-in Mathematica Symbol)

ListLogPlot[{y_1, y_2, ...}] makes a log plot of the y_i, assumed to correspond to x coordinates 1, 2, ....ListLogPlot[{{x_1, y_1}, {x_2, y_2}, ...}] makes a log plot of the ...

Plot (Built-in Mathematica Symbol)

Plot[f, {x, x_min, x_max}] generates a plot of f as a function of x from x_min to x_max. Plot[{f_1, f_2, ...}, {x, x_min, x_max}] plots several functions f_i.

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, ...

Numerical Differential Equation ... (Numerical Differential Equation Analysis Package Tutorial)

The NumericalDifferentialEquationAnalysis package combines functionality for analyzing differential equations using Butcher trees, Gaussian quadrature, and Newton-Cotes ...

Integer and Number Theoretic Functions (Mathematica Tutorial)

Some integer functions. The remainder on dividing 17 by 3. The integer part of 17/3.

ExplicitRungeKutta Method for NDSolve (Mathematica Tutorial)

This loads packages containing some test problems and utility functions. One of the first and simplest methods for solving initial value problems was proposed by Euler: ...

Stiffness Detection (Mathematica Tutorial)

Many differential equations exhibit some form of stiffness, which restricts the step size and hence effectiveness of explicit solution methods. A number of implicit methods ...

FindDistributionParameters (Built-in Mathematica Symbol)

FindDistributionParameters[data, dist] finds the parameter estimates for the distribution dist from data.FindDistributionParameters[data, dist, {{p, p_0}, {q, q_0}, ...}] ...