@stdlib/stats-base-dists-discrete-uniform-ctor NPM

Discrete Uniform

Discrete uniform distribution constructor.

Installation

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

Usage

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

DiscreteUniform( [a, b] )

Returns a discrete uniform distribution object.

var discreteUniform = new DiscreteUniform();

var mu = discreteUniform.mean;
// returns 0.5

By default, a = 0 and b = 1. To create a distribution having a different a (minimum support) and b (maximum support), provide the corresponding arguments.

var discreteUniform = new DiscreteUniform( 2, 4 );

var mu = discreteUniform.mean;
// returns 3.0

discreteUniform

A discrete uniform distribution object has the following properties and methods...

Writable Properties

discreteUniform.a

Minimum support of the distribution. a must be an integer smaller than or equal to b.

var discreteUniform = new DiscreteUniform( -2, 2 );

var a = discreteUniform.a;
// returns -2

discreteUniform.a = 0;

a = discreteUniform.a;
// returns 0

discreteUniform.b

Maximum support of the distribution. b must be an integer larger than or equal to a.

var discreteUniform = new DiscreteUniform( 2, 4 );

var b = discreteUniform.b;
// returns 4

discreteUniform.b = 3;

b = discreteUniform.b;
// returns 3

Computed Properties

DiscreteUniform.prototype.entropy

Returns the differential entropy.

var discreteUniform = new DiscreteUniform( 4, 12 );

var entropy = discreteUniform.entropy;
// returns ~2.197

DiscreteUniform.prototype.kurtosis

Returns the excess kurtosis.

var discreteUniform = new DiscreteUniform( 4, 12 );

var kurtosis = discreteUniform.kurtosis;
// returns -1.23

DiscreteUniform.prototype.mean

Returns the expected value.

var discreteUniform = new DiscreteUniform( 4, 12 );

var mu = discreteUniform.mean;
// returns 8.0

DiscreteUniform.prototype.median

Returns the median.

var discreteUniform = new DiscreteUniform( 4, 12 );

var median = discreteUniform.median;
// returns 8.0

DiscreteUniform.prototype.skewness

Returns the skewness.

var discreteUniform = new DiscreteUniform( 4, 12 );

var skewness = discreteUniform.skewness;
// returns 0.0

DiscreteUniform.prototype.stdev

Returns the standard deviation.

var discreteUniform = new DiscreteUniform( 4, 12 );

var s = discreteUniform.stdev;
// returns ~2.582

DiscreteUniform.prototype.variance

Returns the variance.

var discreteUniform = new DiscreteUniform( 4, 12 );

var s2 = discreteUniform.variance;
// returns ~6.667

Methods

DiscreteUniform.prototype.cdf( x )

Evaluates the cumulative distribution function (CDF).

var discreteUniform = new DiscreteUniform( 2, 4 );

var y = discreteUniform.cdf( 2.5 );
// returns ~0.333

DiscreteUniform.prototype.logcdf( x )

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

var discreteUniform = new DiscreteUniform( 2, 4 );

var y = discreteUniform.logcdf( 2.5 );
// returns ~-1.099

DiscreteUniform.prototype.logpmf( x )

Evaluates the natural logarithm of the probability mass function (PMF).

var discreteUniform = new DiscreteUniform( 2, 4 );

var y = discreteUniform.logpmf( 4.0 );
// returns ~-1.099

DiscreteUniform.prototype.pmf( x )

Evaluates the probability mass function (PMF).

var discreteUniform = new DiscreteUniform( 2, 4 );

var y = discreteUniform.pmf( 3, 0 );
// returns ~0.333

DiscreteUniform.prototype.quantile( p )

Evaluates the quantile function at probability p.

var discreteUniform = new DiscreteUniform( 2, 4 );

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

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

Examples

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

var discreteUniform = new DiscreteUniform( -2, 2 );

var mu = discreteUniform.mean;
// returns 0.0

var median = discreteUniform.median;
// returns 0.0

var s2 = discreteUniform.variance;
// returns 2.0

var y = discreteUniform.cdf( 2.5 );
// returns 1.0

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.