Entropy

Exponential distribution differential entropy.

The differential entropy (in nats) for an exponential random variable is

where λ is the rate parameter.

Installation

npm install @stdlib/stats-base-dists-exponential-entropy

Usage

var entropy = require( '@stdlib/stats-base-dists-exponential-entropy' );

entropy( lambda )

Returns the differential entropy of an exponential distribution with rate parameter lambda (in nats).

var v = entropy( 9.0 );
// returns ~-1.197

v = entropy( 0.5 );
// returns ~1.693

If provided lambda < 0, the function returns NaN.

var v = entropy( -1.0 );
// returns NaN

Examples

var uniform = require( '@stdlib/random-array-uniform' );
var logEachMap = require( '@stdlib/console-log-each-map' );
var entropy = require( '@stdlib/stats-base-dists-exponential-entropy' );

var opts = {
    'dtype': 'float64'
};
var lambda = uniform( 10, 0.0, 20.0, opts );

logEachMap( 'λ: %0.4f, h(X;λ): %0.4f', lambda, entropy );

C APIs

Usage

#include "stdlib/stats/base/dists/exponential/entropy.h"

stdlib_base_dists_exponential_entropy( lambda )

Returns the differential entropy of an exponential distribution with rate parameter lambda (in nats).

double out = stdlib_base_dists_exponential_entropy( 9.0 );
// returns ~-1.197

The function accepts the following arguments:

• lambda: [in] double rate parameter.

double stdlib_base_dists_exponential_entropy( const double lambda );

Examples

#include "stdlib/stats/base/dists/exponential/entropy.h"
#include <stdlib.h>
#include <stdio.h>

static double random_uniform( const double min, const double max ) {
    double v = (double)rand() / ( (double)RAND_MAX + 1.0 );
    return min + ( v*(max-min) );
}

int main( void ) {
    double lambda;
    double y;
    int i;

    for ( i = 0; i < 25; i++ ) {
        lambda = random_uniform( 0.0, 20.0 );
        y = stdlib_base_dists_exponential_entropy( lambda );
        printf( "λ: %lf, h(X;λ): %lf\n", lambda, 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.

Community

License

See LICENSE.

Copyright

Copyright © 2016-2026. The Stdlib Authors.