Exponential distribution variance.
APACHE-2.0 License
Exponential distribution variance.
The variance for an exponential random variable is
\mathop{\mathrm{Var}}\left( X \right) = \lambda^{-2}
where λ
is the rate parameter.
npm install @stdlib/stats-base-dists-exponential-variance
Alternatively,
script
tag without installation and bundlers, use the ES Module available on the esm
branch (see README).deno
branch (see README for usage intructions).umd
branch (see README).The branches.md file summarizes the available branches and displays a diagram illustrating their relationships.
To view installation and usage instructions specific to each branch build, be sure to explicitly navigate to the respective README files on each branch, as linked to above.
var variance = require( '@stdlib/stats-base-dists-exponential-variance' );
Returns the variance of a exponential distribution with rate parameter lambda
.
var v = variance( 9.0 );
// returns ~0.012
v = variance( 0.5 );
// returns 4.0
If provided lambda < 0
, the function returns NaN
.
var v = variance( -1.0 );
// returns NaN
var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var variance = require( '@stdlib/stats-base-dists-exponential-variance' );
var lambda;
var v;
var i;
for ( i = 0; i < 10; i++ ) {
lambda = randu() * 20.0;
v = variance( lambda );
console.log( 'λ: %d, Var(X;λ): %d', lambda.toFixed( 4 ), v.toFixed( 4 ) );
}
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.
See LICENSE.
Copyright © 2016-2024. The Stdlib Authors.