@stdlib/stats-base-dists-laplace-mgf NPM

Moment-Generating Function

Laplace distribution moment-generating function (MGF).

The moment-generating function for a Laplace (double exponential) random variable is

where mu is the location parameter and b is the scale parameter. For |t| >= 1/b, the MGF is undefined.

Installation

npm install @stdlib/stats-base-dists-laplace-mgf

Usage

var mgf = require( '@stdlib/stats-base-dists-laplace-mgf' );

mgf( t, mu, b )

Evaluates the moment-generating function (MGF) for a Laplace (double exponential) distribution with parameters mu (location) and b (scale).

var y = mgf( 0.5, 0.0, 1.0 );
// returns ~1.333

y = mgf( 0.0, 0.0, 1.0 );
// returns 1.0

y = mgf( -1.0, 4.0, 0.2 );
// returns ~0.019

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

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

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

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

If t is not inside the interval (-1/b, 1/b), the function returns NaN.

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

y = mgf( -0.5, 0.0, 4.0 );
// returns NaN

If provided b <= 0, the function returns NaN.

var y = mgf( 2.0, 0.0, 0.0 );
// returns NaN

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

mgf.factory( mu, b )

Returns a function for evaluating the moment-generating function (MGF) of a Laplace (double exponential) distribution with parameters mu and b.

var mymgf = mgf.factory( 4.0, 2.0 );

var y = mymgf( 0.2 );
// returns ~2.649

y = mymgf( 0.4 );
// returns ~13.758

Examples

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

var mu;
var b;
var t;
var y;
var i;

for ( i = 0; i < 10; i++ ) {
    t = randu();
    mu = (randu() * 10.0) - 5.0;
    b = randu() * 20.0;
    y = mgf( t, mu, b );
    console.log( 't: %d, µ: %d, b: %d, M_X(t;µ,b): %d', t.toFixed( 4 ), mu.toFixed( 4 ), b.toFixed( 4 ), y.toFixed( 4 ) );
}

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.