Numerical Nonlinear Local Optimization (Mathematica Tutorial)

Numerical algorithms for constrained nonlinear optimization can be broadly categorized into gradient-based methods and direct search methods. Gradient search methods use ...

CountryData (Built-in Mathematica Symbol)

CountryData["tag", " property"] gives the value of the specified property for the country, country-like entity, or group of countries specified by " tag".CountryData["tag", ...

ListPlot3D (Built-in Mathematica Symbol)

ListPlot3D[array] generates a three-dimensional plot of a surface representing an array of height values. ListPlot3D[{{x_1, y_1, z_1}, {x_2, y_2, z_2}, ...}] generates a plot ...

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 ...

PearsonDistribution (Built-in Mathematica Symbol)

PearsonDistribution[a_1, a_0, b_2, b_1, b_0] represents a distribution of the Pearson family with parameters a_1, a_0, b_2, b_1, and b_0.PearsonDistribution[type, a_1, a_0, ...

Linear Programming (Mathematica Tutorial)

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

Statistical Model Analysis (Mathematica Tutorial)

When fitting models to data, it is often useful to analyze how well the model fits the data and how well the fitting meets the assumptions of the model. For a number of ...

ContourPlot (Built-in Mathematica Symbol)

ContourPlot[f, {x, x_min, x_max}, {y, y_min, y_max}] generates a contour plot of f as a function of x and y. ContourPlot[f == g, {x, x_min, x_max}, {y, y_min, y_max}] plots ...

Probability (Built-in Mathematica Symbol)

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

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, ...