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Evaluate the natural logarithm of the probability mass function (PMF) for a binomial distribution.
The probability mass function (PMF) for a binomial random variable is
where n is the number of trials and 0 <= p <= 1 is the success probability.
To use in Observable,
logpmf = require( 'https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-dists-binomial-logpmf@umd/browser.js' )To vendor stdlib functionality and avoid installing dependency trees for Node.js, you can use the UMD server build:
var logpmf = require( 'path/to/vendor/umd/stats-base-dists-binomial-logpmf/index.js' )To include the bundle in a webpage,
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-dists-binomial-logpmf@umd/browser.js"></script>If no recognized module system is present, access bundle contents via the global scope:
<script type="text/javascript">
(function () {
window.logpmf;
})();
</script>Evaluates the natural logarithm of the probability mass function (PMF) for a binomial distribution with number of trials n and success probability p.
var y = logpmf( 3.0, 20, 0.2 );
// returns ~-1.583
y = logpmf( 21.0, 20, 0.2 );
// returns -Infinity
y = logpmf( 5.0, 10, 0.4 );
// returns ~-1.606
y = logpmf( 0.0, 10, 0.4 );
// returns ~-5.108If provided NaN as any argument, the function returns NaN.
var y = logpmf( NaN, 20, 0.5 );
// returns NaN
y = logpmf( 0.0, NaN, 0.5 );
// returns NaN
y = logpmf( 0.0, 20, NaN );
// returns NaNIf provided a number of trials n which is not a nonnegative integer, the function returns NaN.
var y = logpmf( 2.0, 1.5, 0.5 );
// returns NaN
y = logpmf( 2.0, -2.0, 0.5 );
// returns NaNIf provided a success probability p outside of [0,1], the function returns NaN.
var y = logpmf( 2.0, 20, -1.0 );
// returns NaN
y = logpmf( 2.0, 20, 1.5 );
// returns NaNReturns a function for evaluating the probability mass function (PMF) of a binomial distribution with number of trials n and success probability p.
var mylogpmf = logpmf.factory( 10, 0.5 );
var y = mylogpmf( 3.0 );
// returns ~-2.144
y = mylogpmf( 5.0 );
// returns ~-1.402<!DOCTYPE html>
<html lang="en">
<body>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/random-base-randu@umd/browser.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/math-base-special-round@umd/browser.js"></script>
<script type="text/javascript" src="https://cdn.jsdelivr.net/gh/stdlib-js/stats-base-dists-binomial-logpmf@umd/browser.js"></script>
<script type="text/javascript">
(function () {
var i;
var n;
var p;
var x;
var y;
for ( i = 0; i < 10; i++ ) {
x = round( randu() * 20.0 );
n = round( randu() * 100.0 );
p = randu();
y = logpmf( x, n, p );
console.log( 'x: %d, n: %d, p: %d, ln(P(X = x;n,p)): %d', x, n, p.toFixed( 4 ), y.toFixed( 4 ) );
}
})();
</script>
</body>
</html>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.
