Introduction to DatabaseLink (DatabaseLink Tutorial)

Data storage, indexing, and retrieval have long been crucial tasks of many large organizations such as governments, banks, hospitals, and libraries. As human societies have ...

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 ...

DistributionFitTest (Built-in Mathematica Symbol)

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

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}, ...}] ...

TradingChart (Built-in Mathematica Symbol)

TradingChart[{{date_1, {open_1, high_1, low_1, close_1, volume_1}}, ...}] makes a chart showing prices and volume for each date. TradingChart[{" name", daterange}] makes a ...

KagiChart (Built-in Mathematica Symbol)

KagiChart[{{date_1, p_1}, {date_2, p_2}, ...}] makes a Kagi chart with prices p_i at date date_i.KagiChart[{" name", daterange}] makes a Kagi chart of closing prices for the ...

Parallel Computation (Compiled Function Tools Tutorial)

The Mathematica compiler can run computations in parallel. It does this by threading a compiled function over lists of data in parallel. A first step is to create a compiled ...

BinormalDistribution (Built-in Mathematica Symbol)

BinormalDistribution[{\[Mu]_1, \[Mu]_2}, {\[Sigma]_1, \[Sigma]_\ 2}, \[Rho]] represents a bivariate normal distribution with mean {\[Mu]_1, \[Mu]_2} and covariance matrix ...

CompleteKaryTree (Built-in Mathematica Symbol)

CompleteKaryTree[n] gives the complete binary tree with n levels.CompleteKaryTree[n, k] gives the complete k-ary tree with n levels.

FindFit (Built-in Mathematica Symbol)

FindFit[data, expr, pars, vars] finds numerical values of the parameters pars that make expr give a best fit to data as a function of vars. The data can have the form {{x_1, ...