Gamma distribution variance.
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[Gamma][gamma-distribution] distribution [variance][variance].
math
\mathop{\mathrm{Var}}\left( X \right) = \frac{\alpha}{\beta^2}
α > 0
is the shape parameter β > 0
is the rate parameter.bash
npm install @stdlib/stats-base-dists-gamma-variance
script
tag without installation and bundlers, use the [ES Module][es-module] available on the [esm
][esm-url] branch (see [README][esm-readme]).deno
][deno-url] branch (see [README][deno-readme] for usage intructions).umd
][umd-url] branch (see [README][umd-readme]).javascript
var variance = require( '@stdlib/stats-base-dists-gamma-variance' );
alpha
(shape parameter) and beta
(rate parameter).javascript
var v = variance( 1.0, 1.0 );
// returns 1.0
v = variance( 4.0, 12.0 );
// returns ~0.028
v = variance( 8.0, 2.0 );
// returns 2.0
NaN
as any argument, the function returns NaN
.javascript
var v = variance( NaN, 2.0 );
// returns NaN
v = variance( 2.0, NaN );
// returns NaN
alpha <= 0
, the function returns NaN
.javascript
var v = variance( 0.0, 1.0 );
// returns NaN
v = variance( -1.0, 1.0 );
// returns NaN
beta <= 0
, the function returns NaN
.javascript
var v = variance( 1.0, 0.0 );
// returns NaN
v = variance( 1.0, -1.0 );
// returns NaN
javascript
var randu = require( '@stdlib/random-base-randu' );
var EPS = require( '@stdlib/constants-float64-eps' );
var variance = require( '@stdlib/stats-base-dists-gamma-variance' );
var alpha;
var beta;
var v;
var i;
for ( i = 0; i < 10; i++ ) {
alpha = ( randu()*10.0 ) + EPS;
beta = ( randu()*10.0 ) + EPS;
v = variance( alpha, beta );
console.log( 'α: %d, β: %d, Var(X;α,β): %d', alpha.toFixed( 4 ), beta.toFixed( 4 ), v.toFixed( 4 ) );
}
c
#include "stdlib/stats/base/dists/gamma/variance.h"
c
double out = stdlib_base_dists_gamma_variance( 1.0, 1.0 );
// returns 1.0
[in] double
shape parameter.[in] double
rate parameter.c
double stdlib_base_dists_gamma_variance( const double alpha, const double beta );
c
#include "stdlib/stats/base/dists/gamma/variance.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 alpha;
double beta;
double y;
int i;
for ( i = 0; i < 25; i++ ) {
alpha = random_uniform( 0.0, 20.0 );
beta = random_uniform( 0.0, 20.0 );
y = stdlib_base_dists_gamma_variance( alpha, beta );
printf( "α: %lf, β: %lf, Var(X;α,β): %lf\n", alpha, beta, y );
}
}