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Continuous Distributions   (Mathematica Tutorial)
The functions described here are among the most commonly used continuous univariate statistical distributions. You can compute their densities, means, variances, and other ...
Developing an Import Converter   (Mathematica Tutorial)
Mathematica provides functions that allow users to write their own file format converters and integrate them with the Mathematica Import and Export framework. You can ...
Linear Systems of ODEs   (Mathematica Tutorial)
Here is a system of two ODEs whose coefficient matrix has real and distinct eigenvalues. This solves the system. Note that the general solution depends on two arbitrary ...
Every new version of Mathematica contains many new features. But careful design from the outset has allowed nearly total compatibility to be maintained between all versions. ...
Introduction to Advanced Numerical ...   (Mathematica Tutorial)
The Mathematica function NDSolve is a general numerical differential equation solver. It can handle a wide range of ordinary differential equations (ODEs) as well as some ...
Data Type Mapping   (DatabaseLink Tutorial)
One of the most important issues for using a database is the conversion of data as it is stored and retrieved from a database. This tutorial will discuss how Mathematica ...
Representing XML in Mathematica   (XML Package Tutorial)
Mathematica includes comprehensive support for XML, the meta-markup language developed by the World Wide Web Consortium (W3C) for describing structured documents and data. ...
Alphabetical Listing   (Mathematica Guide)
Mathematica has over 3000 built-in functions and other objects, all based on a single unified framework, and all carefully designed to work together, both in simple ...
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 ...
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, ...}, ...
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