README

Probability Mass Function

Hypergeometric distribution probability mass function (PMF).

Imagine a scenario with a population of size N, of which a subpopulation of size K can be considered successes. We draw n observations from the total population. Defining the random variable X as the number of successes in the n draws, X is said to follow a hypergeometric distribution. The probability mass function (PMF) for a hypergeometric random variable is given by

Probability mass function (PMF) for a hypergeometric distribution.

Installation

npm install @stdlib/stats-base-dists-hypergeometric-pmf

Usage

var pmf = require( '@stdlib/stats-base-dists-hypergeometric-pmf' );

pmf( x, N, K, n )

Evaluates the probability mass function (PMF) for a hypergeometric distribution with parameters N (population size), K (subpopulation size), and n (number of draws).

var y = pmf( 1.0, 8, 4, 2 );
// returns ~0.571

y = pmf( 2.0, 8, 4, 2 );
// returns ~0.214

y = pmf( 0.0, 8, 4, 2 );
// returns ~0.214

y = pmf( 1.5, 8, 4, 2 );
// returns 0.0

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

var y = pmf( NaN, 10, 5, 2 );
// returns NaN

y = pmf( 0.0, NaN, 5, 2 );
// returns NaN

y = pmf( 0.0, 10, NaN, 2 );
// returns NaN

y = pmf( 0.0, 10, 5, NaN );
// returns NaN

If provided a population size N, subpopulation size K or draws n which is not a nonnegative integer, the function returns NaN.

var y = pmf( 2.0, 10.5, 5, 2 );
// returns NaN

y = pmf( 2.0, 10, 1.5, 2 );
// returns NaN

y = pmf( 2.0, 10, 5, -2.0 );
// returns NaN

If the number of draws n exceeds population size N, the function returns NaN.

var y = pmf( 2.0, 10, 5, 12 );
// returns NaN

y = pmf( 2.0, 8, 3, 9 );
// returns NaN

pmf.factory( N, K, n )

Returns a function for evaluating the probability mass function (PMF) of a hypergeometric distribution with parameters N (population size), K (subpopulation size), and n (number of draws).

var mypmf = pmf.factory( 30, 20, 5 );
var y = mypmf( 4.0 );
// returns ~0.34

y = mypmf( 1.0 );
// returns ~0.029

Examples

var randu = require( '@stdlib/random-base-randu' );
var round = require( '@stdlib/math-base-special-round' );
var pmf = require( '@stdlib/stats-base-dists-hypergeometric-pmf' );

var i;
var N;
var K;
var n;
var x;
var y;

for ( i = 0; i < 10; i++ ) {
    x = round( randu() * 5.0 );
    N = round( randu() * 20.0 );
    K = round( randu() * N );
    n = round( randu() * N );
    y = pmf( x, N, K, n );
    console.log( 'x: %d, N: %d, K: %d, n: %d, P(X=x;N,K,n): %d', x, N, K, n, y.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-hypergeometric-pmf

Usage no npm install needed!