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Mean
Kumaraswamy's double bounded distribution expected value.
The mean for a Kumaraswamy's double bounded random variable is
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.
Copyright
Copyright © 2016-2024. The Stdlib Authors.