Mean

Weibull distribution expected value.

The expected value for a Weibull random variable is

where k > 0 is the shape parameter λ > 0 is the scale parameter.

Usage

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

mean( k, lambda )

Returns the expected value of a Weibull distribution with parameters k (shape parameter) and lambda (scale parameter).

var v = mean( 1.0, 1.0 );
// returns 1.0

v = mean( 4.0, 12.0 );
// returns ~10.877

v = mean( 8.0, 2.0 );
// returns ~1.883

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 k <= 0, the function returns NaN.

var v = mean( 0.0, 1.0 );
// returns NaN

v = mean( -1.0, 1.0 );
// returns NaN

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

var v = mean( 1.0, 0.0 );
// returns NaN

v = mean( 1.0, -1.0 );
// returns NaN

Examples

var randu = require( '@stdlib/random/base/randu' );
var EPS = require( '@stdlib/constants/float64/eps' );
var mean = require( '@stdlib/stats/base/dists/weibull/mean' );

var lambda;
var k;
var v;
var i;

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

C APIs

Usage

#include "stdlib/stats/base/dists/weibull/mean.h"

stdlib_base_dists_weibull_mean( k, lambda )

Returns the expected value of a Weibull distribution with parameters k (shape parameter) and lambda (scale parameter).

double out = stdlib_base_dists_weibull_mean( 4.0, 12.0 );
// returns ~10.877

The function accepts the following arguments:

k: [in] double shape parameter.
lambda: [in] double scale parameter.

double stdlib_base_dists_weibull_mean( const double k, const double lambda );

Examples

#include "stdlib/stats/base/dists/weibull/mean.h"
#include "stdlib/constants/float64/eps.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 lambda;
    double k;
    double y;
    int i;

    for ( i = 0; i < 25; i++ ) {
        k = random_uniform( STDLIB_CONSTANT_FLOAT64_EPS, 10.0 );
        lambda = random_uniform( STDLIB_CONSTANT_FLOAT64_EPS, 10.0 );
        y = stdlib_base_dists_weibull_mean( k, lambda );
        printf( "k: %lf, λ: %lf, E(X;k,λ): %lf\n", k, lambda, y );
    }
}