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Uniform

Uniform distribution constructor.

Installation

npm install @stdlib/stats-base-dists-uniform-ctor

Alternatively,

To load the package in a website via a script tag without installation and bundlers, use the ES Module available on the esm branch (see README).
If you are using Deno, visit the deno branch (see README for usage intructions).
For use in Observable, or in browser/node environments, use the Universal Module Definition (UMD) build available on the umd branch (see README).

The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.

To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.

Usage

var Uniform = require( '@stdlib/stats-base-dists-uniform-ctor' );

Uniform( [a, b] )

Returns an uniform distribution object.

var uniform = new Uniform();

var mu = uniform.mean;
// returns 0.5

By default, a = 0.0 and b = 1.0. To create a distribution having a different a (minimum support) and b (maximum support), provide the corresponding arguments.

var uniform = new Uniform( 2.0, 4.0 );

var mu = uniform.mean;
// returns 3.0

uniform

An uniform distribution object has the following properties and methods...

Writable Properties

uniform.a

Minimum support of the distribution. a must be a number smaller than b.

var uniform = new Uniform();

var a = uniform.a;
// returns 0.0

uniform.a = 0.5;

a = uniform.a;
// returns 0.5

uniform.b

Maximum support of the distribution. b must be a number larger than a.

var uniform = new Uniform( 2.0, 4.0 );

var b = uniform.b;
// returns 4.0

uniform.b = 3.0;

b = uniform.b;
// returns 3.0

Computed Properties

Uniform.prototype.entropy

Returns the differential entropy.

var uniform = new Uniform( 4.0, 12.0 );

var entropy = uniform.entropy;
// returns ~2.079

Uniform.prototype.kurtosis

Returns the excess kurtosis.

var uniform = new Uniform( 4.0, 12.0 );

var kurtosis = uniform.kurtosis;
// returns -1.2

Uniform.prototype.mean

Returns the expected value.

var uniform = new Uniform( 4.0, 12.0 );

var mu = uniform.mean;
// returns 8.0

Uniform.prototype.median

Returns the median.

var uniform = new Uniform( 4.0, 12.0 );

var median = uniform.median;
// returns 8.0

Uniform.prototype.skewness

Returns the skewness.

var uniform = new Uniform( 4.0, 12.0 );

var skewness = uniform.skewness;
// returns 0.0

Uniform.prototype.stdev

Returns the standard deviation.

var uniform = new Uniform( 4.0, 12.0 );

var s = uniform.stdev;
// returns ~2.309

Uniform.prototype.variance

Returns the variance.

var uniform = new Uniform( 4.0, 12.0 );

var s2 = uniform.variance;
// returns ~5.333

Methods

Uniform.prototype.cdf( x )

Evaluates the cumulative distribution function (CDF).

var uniform = new Uniform( 2.0, 4.0 );

var y = uniform.cdf( 2.5 );
// returns 0.25

Uniform.prototype.logcdf( x )

Evaluates the natural logarithm of the cumulative distribution function (CDF).

var uniform = new Uniform( 2.0, 4.0 );

var y = uniform.logcdf( 2.5 );
// returns ~-1.386

Uniform.prototype.logpdf( x )

Evaluates the natural logarithm of the probability density function (PDF).

var uniform = new Uniform( 2.0, 4.0 );

var y = uniform.logpdf( 2.5 );
// returns ~-0.693

Uniform.prototype.pdf( x )

Evaluates the probability density function (PDF).

var uniform = new Uniform( 2.0, 4.0 );

var y = uniform.pdf( 2.5 );
// returns 0.5

Uniform.prototype.quantile( p )

Evaluates the quantile function at probability p.

var uniform = new Uniform( 2.0, 4.0 );

var y = uniform.quantile( 0.5 );
// returns 3.0

y = uniform.quantile( 1.9 );
// returns NaN

Examples

var Uniform = require( '@stdlib/stats-base-dists-uniform-ctor' );

var uniform = new Uniform( 2.0, 4.0 );

var mu = uniform.mean;
// returns 3.0

var median = uniform.median;
// returns 3.0

var s2 = uniform.variance;
// returns ~0.333

var y = uniform.cdf( 2.5 );
// returns 0.25

Notice

This package is part of stdlib, a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop stdlib, see the main project repository.

Community

License

See LICENSE.

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