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    @stdlib/stats-base-dists-bernoulli-pmf
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    0.0.8 • Public • Published

    Probability Mass Function

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    Bernoulli distribution probability mass function (PMF).

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

    Probability mass function (PMF) for a Bernoulli distribution.

    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.

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    License

    See LICENSE.

    Copyright

    Copyright © 2016-2022. The Stdlib Authors.

    Install

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

    Homepage

    stdlib.io

    DownloadsWeekly Downloads

    210

    Version

    0.0.8

    License

    Apache-2.0

    Unpacked Size

    44.3 kB

    Total Files

    11

    Last publish

    Collaborators

    • stdlib-bot
    • kgryte
    • planeshifter
    • rreusser