README.md

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Entropy

Laplace distribution differential entropy.

The differential entropy (in nats) for a Laplace random variable with location μ and scale b > 0 is

$$h\left( X \right) = \ln(2be)$$

where e is Euler's number.

Usage

import entropy from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-dists-laplace-entropy@deno/mod.js';

entropy( mu, b )

Returns the differential entropy for a Laplace distribution with location parameter mu and scale parameter b (in nats).

var y = entropy( 2.0, 1.0 );
// returns ~1.693

y = entropy( 0.0, 1.0 );
// returns ~1.693

y = entropy( -1.0, 4.0 );
// returns ~3.079

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 b <= 0, the function returns NaN.

var y = entropy( 0.0, 0.0 );
// returns NaN

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

Examples

import randu from 'https://cdn.jsdelivr.net/gh/stdlib-js/random-base-randu@deno/mod.js';
import entropy from 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-dists-laplace-entropy@deno/mod.js';

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

for ( i = 0; i < 10; i++ ) {
    mu = ( randu()*10.0 ) - 5.0;
    b = randu() * 20.0;
    y = entropy( mu, b );
    console.log( 'µ: %d, b: %d, h(X;µ,b): %d', mu.toFixed( 4 ), b.toFixed( 4 ), y.toFixed( 4 ) );
}

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Notice

This package is part of stdlib, a standard library 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.

Community

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.