@stdlib/stats-base-dists-bernoulli-pmf
v0.3.1TypeScript
Bernoulli distribution probability mass function (PMF).
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Published by The Stdlib Authors
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Probability Mass Function
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> [Bernoulli][bernoulli-distribution] distribution [probability mass function][pmf] (PMF).
The [probability mass function][pmf] (PMF) for a [Bernoulli][bernoulli-distribution] random variable is defined as
where 0 <= p <= 1 is the success probability.
Installation
``bash`
npm install @stdlib/stats-base-dists-bernoulli-pmf
Usage
`javascript`
var pmf = require( '@stdlib/stats-base-dists-bernoulli-pmf' );
#### pmf( x, p )
Evaluates the [probability mass function][pmf] (PMF) of a [Bernoulli][bernoulli-distribution] distribution with success probability 0 <= p <= 1.
`javascript
var y = pmf( 1.0, 0.3 );
// returns 0.3
y = pmf( 0.0, 0.3 );
// returns 0.7
y = pmf( -1.0, 0.5 );
// returns 0.0
`
If provided NaN as any argument, the function returns NaN.
`javascript
var y = pmf( NaN, 0.0 );
// returns NaN
y = pmf( 0.0, NaN );
// returns NaN
`
If provided a success probability p outside of the interval [0,1], the function returns NaN.
`javascript
var y = pmf( 0.0, -1.0 );
// returns NaN
y = pmf( 0.0, 1.5 );
// returns NaN
`
#### pmf.factory( p )
Returns a function for evaluating the [probability mass function][pmf] (PMF) of a [Bernoulli][bernoulli-distribution] distribution with success probability 0 <= p <= 1.
`javascript
var mypmf = pmf.factory( 0.8 );
var y = mypmf( 0.0 );
// returns 0.2
y = mypmf( 0.5 );
// returns 0.0
`
Examples
`javascript
var uniform = require( '@stdlib/random-array-uniform' );
var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var logEachMap = require( '@stdlib/console-log-each-map' );
var pmf = require( '@stdlib/stats-base-dists-bernoulli-pmf' );
var opts = {
'dtype': 'float64'
};
var p = uniform( 25, 0.0, 1.0, opts );
var x = discreteUniform( p.length, 0, 1 );
logEachMap( 'x: %d, p: %0.4f, P( X = x; p ): %0.4f', x, p, pmf );
`
*
C APIs
$3
`c`
#include "stdlib/stats/base/dists/bernoulli/pmf.h"
#### stdlib_base_dists_bernoulli_pmf( x, p )
Evaluates the [probability mass function][pmf] (PMF) of a [Bernoulli][bernoulli-distribution] distribution with success probability 0 <= p <= 1.
`c`
double y = stdlib_base_dists_bernoulli_pmf( 1.0, 0.3 );
// returns 0.3
The function accepts the following arguments:
- x: [in] double input value.[in] double
- p: success probability.
`c`
double stdlib_base_dists_bernoulli_pmf( const double x, const double p );
$3
`c
#include "stdlib/stats/base/dists/bernoulli/pmf.h"
#include "stdlib/math/base/special/round.h"
#include
#include
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 p;
double x;
double y;
int i;
for ( i = 0; i < 10; i++ ) {
x = stdlib_base_round( random_uniform( 0.0, 2.0 ) );
p = random_uniform( 0.0, 1.0 );
y = stdlib_base_dists_bernoulli_pmf( x, p );
printf( "x: %lf, p: %lf, P( X = x; p ): %lf\n", x, p, y );
}
}
`
*
Notice
This package is part of [stdlib][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][stdlib], see the main project [repository][stdlib].
#### Community
[![Chat][chat-image]][chat-url]
---
License
See [LICENSE][stdlib-license].
Copyright
Copyright © 2016-2026. The Stdlib [Authors][stdlib-authors].
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[bernoulli-distribution]: https://en.wikipedia.org/wiki/Bernoulli_distribution
[pmf]: https://en.wikipedia.org/wiki/Probability_mass_function