Entropy

Half-normal distribution differential entropy.

The differential entropy (in nats) for a half-normal random variable is

where σ > 0 is the scale parameter.

Installation

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

Usage

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

entropy( sigma )

Returns the differential entropy of a half-normal distribution with scale sigma (in nats).

var y = entropy( 1.0 );
// returns ~0.7258

y = entropy( 5.0 );
// returns ~2.3352

If provided sigma ≤ 0, the function returns NaN.

var y = 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-halfnormal-entropy' );

var opts = {
    'dtype': 'float64'
};
var sigma = uniform( 10, 0.1, 20.0, opts );

logEachMap( 'σ: %0.4f, h(X;σ): %0.4f', sigma, entropy );

C APIs

Usage

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

stdlib_base_dists_halfnormal_entropy( sigma )

Returns the differential entropy of a half-normal distribution.

double out = stdlib_base_dists_halfnormal_entropy( 1.0 );
// returns ~0.7258

The function accepts the following arguments:

• sigma: [in] double scale parameter.

double stdlib_base_dists_halfnormal_entropy( const double sigma );

Examples

#include "stdlib/stats/base/dists/halfnormal/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 sigma;
    double y;
    int i;

    for ( i = 0; i < 25; i++ ) {
        sigma = random_uniform( 0.1, 20.0 );
        y = stdlib_base_dists_halfnormal_entropy( sigma );
        printf( "σ: %lf, h(σ): %lf\n", sigma, 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.