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@stdlib/stats-base-dists-logistic-pdf

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Readme

Probability Density Function

Logistic distribution probability density function (PDF).

The probability density function (PDF) for a logistic random variable is

Probability density function (PDF) for a logistic distribution.

where mu is the location parameter and s is the scale parameter.

Installation

npm install @stdlib/stats-base-dists-logistic-pdf

Usage

var pdf = require( '@stdlib/stats-base-dists-logistic-pdf' );

pdf( x, mu, s )

Evaluates the probability density function (PDF) for a logistic distribution with parameters mu (location parameter) and s (scale parameter).

var y = pdf( 2.0, 0.0, 1.0 );
// returns ~0.105

y = pdf( -1.0, 4.0, 4.0 );
// returns ~0.043

If provided NaN as any argument, the function returns NaN.

var y = pdf( NaN, 0.0, 1.0 );
// returns NaN

y = pdf( 0.0, NaN, 1.0 );
// returns NaN

y = pdf( 0.0, 0.0, NaN );
// returns NaN

If provided s < 0, the function returns NaN.

var y = pdf( 2.0, 0.0, -1.0 );
// returns NaN

If provided s = 0, the function evaluates the PDF of a degenerate distribution centered at mu.

var y = pdf( 2.0, 8.0, 0.0 );
// returns 0.0

y = pdf( 8.0, 8.0, 0.0 );
// returns Infinity

pdf.factory( mu, s )

Returns a function for evaluating the probability density function (PDF) of a logistic distribution with parameters mu (location parameter) and s (scale parameter).

var mypdf = pdf.factory( 10.0, 2.0 );

var y = mypdf( 10.0 );
// returns 0.125

y = mypdf( 5.0 );
// returns ~0.035

Examples

var randu = require( '@stdlib/random-base-randu' );
var pdf = require( '@stdlib/stats-base-dists-logistic-pdf' );

var mu;
var s;
var x;
var y;
var i;

for ( i = 0; i < 10; i++ ) {
    x = randu() * 10.0;
    mu = randu() * 10.0;
    s = randu() * 10.0;
    y = pdf( x, mu, s );
    console.log( 'x: %d, µ: %d, s: %d, f(x;µ,s): %d', x, mu, s, y );
}

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.

License

See LICENSE.

@stdlib/stats-base-dists-logistic-pdf