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Gumbel distribution differential entropy.
The differential entropy for a gumbel random variable with location μ
and scale β
is
where γ
is the Euler–Mascheroni constant.
To use in Observable,
entropy = require( 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-dists-gumbel-entropy@umd/browser.js' )
To vendor stdlib functionality and avoid installing dependency trees for Node.js, you can use the UMD server build:
var entropy = require( 'path/to/vendor/umd/stats-base-dists-gumbel-entropy/index.js' )
To include the bundle in a webpage,
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-dists-gumbel-entropy@umd/browser.js"></script>
If no recognized module system is present, access bundle contents via the global scope:
<script type="text/javascript">
(function () {
window.entropy;
})();
</script>
Returns the differential entropy for a Gumbel distribution with location parameter mu
and scale parameter beta
(in nats).
var y = entropy( 2.0, 1.0 );
// returns ~1.577
y = entropy( 0.0, 1.0 );
// returns ~1.577
y = entropy( -1.0, 4.0 );
// returns ~2.964
If provided NaN
as any argument, the function returns NaN
.
var y = entropy( NaN, 1.0 );
// returns NaN
y = entropy( 0.0, NaN );
// returns NaN
If provided beta <= 0
, the function returns NaN
.
var y = entropy( 0.0, 0.0 );
// returns NaN
y = entropy( 0.0, -1.0 );
// returns NaN
<!DOCTYPE html>
<html lang="en">
<body>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/random-base-randu@umd/browser.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-dists-gumbel-entropy@umd/browser.js"></script>
<script type="text/javascript">
(function () {
var beta;
var mu;
var y;
var i;
for ( i = 0; i < 10; i++ ) {
mu = ( randu()*10.0 ) - 5.0;
beta = randu() * 20.0;
y = entropy( mu, beta );
console.log( 'µ: %d, β: %d, h(X;µ,β): %d', mu.toFixed( 4 ), beta.toFixed( 4 ), y.toFixed( 4 ) );
}
})();
</script>
</body>
</html>
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.
See LICENSE.
Copyright © 2016-2024. The Stdlib Authors.