README

Mean

Kumaraswamy's double bounded distribution expected value.

The mean for a Kumaraswamy's double bounded random variable is

Mean for a Kumaraswamy's double bounded distribution.

where a is the first shape parameter, b the second shape parameter, and Γ denotes the gamma function.

Installation

npm install @stdlib/stats-base-dists-kumaraswamy-mean

Usage

var mean = require( '@stdlib/stats-base-dists-kumaraswamy-mean' );

mean( a, b )

Returns the expected value of a Kumaraswamy's double bounded distribution with first shape parameter a and second shape parameter b.

var v = mean( 1.5, 1.5 );
// returns ~0.512

v = mean( 4.0, 12.0 );
// returns ~0.481

v = mean( 2.0, 8.0 );
// returns ~0.3

If provided NaN as any argument, the function returns NaN.

var v = mean( NaN, 2.0 );
// returns NaN

v = mean( 2.0, NaN );
// returns NaN

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

var y = mean( -1.0, 2.0 );
// returns NaN

y = mean( 0.0, 2.0 );
// returns NaN

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

var y = mean( 2.0, -1.0 );
// returns NaN

y = mean( 2.0, 0.0 );
// returns NaN

Examples

var randu = require( '@stdlib/random-base-randu' );
var mean = require( '@stdlib/stats-base-dists-kumaraswamy-mean' );

var a;
var b;
var v;
var i;

for ( i = 0; i < 10; i++ ) {
    a = randu() * 10.0;
    b = randu() * 10.0;
    v = mean( a, b );
    console.log( 'a: %d, b: %d, E(X;a,b): %d', a.toFixed( 4 ), b.toFixed( 4 ), v.toFixed( 4 ) );
}

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.

@stdlib/stats-base-dists-kumaraswamy-mean

Usage no npm install needed!