# @stdlib/stats-base-dists-bernoulli-pmf

0.2.1 • Public • Published
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# Probability Mass Function

Bernoulli distribution probability mass function (PMF).

The probability mass function (PMF) for a Bernoulli random variable is defined as

where `0 <= p <= 1` is the success probability.

## Installation

`npm install @stdlib/stats-base-dists-bernoulli-pmf`

## Usage

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

#### pmf( x, p )

Evaluates the probability mass function (PMF) of a Bernoulli distribution with success probability `0 <= p <= 1`.

```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`.

```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`.

```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) of a Bernoulli distribution with success probability `0 <= p <= 1`.

```var mypmf = pmf.factory( 0.8 );
var y = mypmf( 0.0 );
// returns 0.2

y = mypmf( 0.5 );
// returns 0.0```

## Examples

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

var p;
var x;
var y;
var i;

for ( i = 0; i < 10; i++ ) {
x = round( randu() * 2.0 );
p = randu();
y = pmf( x, p );
console.log( 'x: %d, p: %d, P( X = x; p ): %d', x, p.toFixed( 4 ), 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.

See LICENSE.

## Copyright

Copyright © 2016-2024. The Stdlib Authors.

## Package Sidebar

### Install

`npm i @stdlib/stats-base-dists-bernoulli-pmf`

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