time-to-botec/js/node_modules/@stdlib/stats/base/dists/gumbel/mgf
NunoSempere b6addc7f05 feat: add the node modules
Necessary in order to clearly see the squiggle hotwiring.
2022-12-03 12:44:49 +00:00
..
docs feat: add the node modules 2022-12-03 12:44:49 +00:00
lib feat: add the node modules 2022-12-03 12:44:49 +00:00
package.json feat: add the node modules 2022-12-03 12:44:49 +00:00
README.md feat: add the node modules 2022-12-03 12:44:49 +00:00

Moment-Generating Function

Gumbel distribution moment-generating function (MGF).

The moment-generating function for a Gumbel random variable is

Moment-generating function (MGF) for a Gumbel distribution.

where mu is the location parameter and beta > 0 is the scale parameter.

Usage

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

mgf( t, mu, beta )

Evaluates the moment-generating function (MGF) for a Gumbel distribution with parameters mu (location parameter) and beta > 0 (scale parameter).

var y = mgf( -1.0, 0.0, 3.0 );
// returns 6.0

y = mgf( 0.1, 0.0, 3.0 );
// returns ~1.298

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

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 provided t >= 1/beta, the function returns NaN.

var y = mgf( 0.8, 0.0, 2.0 );
// returns NaN

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

var y = mgf( 0.5, 0.0, -1.0 );
// returns NaN

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

mgf.factory( mu, beta )

Returns a function for evaluating the moment-generating function of a Gumbel distribution with parameters mu (location parameter) and beta > 0 (scale parameter).

var myMGF = mgf.factory( 0.0, 2.0 );
var y = myMGF( 0.2 );
// returns ~1.489

y = myMGF( -1.0 );
// returns 2.0

Examples

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

var beta;
var mu;
var t;
var y;
var i;

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