About stdlib...

We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.

The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.

When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.

To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

Probability Mass Function

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url]

> [Geometric][geometric-distribution] distribution [probability mass function][pmf] (PMF).

The [probability mass function][pmf] (PMF) for a [geometric][geometric-distribution] random variable is defined as

Probability mass function (PMF) for a geometric distribution.

where 0 <= p <= 1 is the success probability. The random variable X denotes the number of failures until the first success in a sequence of independent Bernoulli trials.

Installation

``bash npm install @stdlib/stats-base-dists-geometric-pmf`

`Usage`

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

#### pmf( x, p )

Evaluates the [probability mass function][pmf] (PMF) of a [geometric][geometric-distribution] distribution with success probability 0 <= p <= 1.

`javascript var y = pmf( 4.0, 0.3 ); // returns ~0.072

y = pmf( 2.0, 0.7 ); // returns ~0.063

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( 2.0, -1.0 ); // returns NaN

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

#### pmf.factory( p )

Returns a function for evaluating the [probability mass function][pmf] (PMF) of a [geometric][geometric-distribution] distribution with success probability 0 <= p <= 1.

`javascript var mypmf = pmf.factory( 0.5 ); var y = mypmf( 3.0 ); // returns 0.0625

y = mypmf( 1.0 ); // returns 0.25`

`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-geometric-pmf' );

var opts = { 'dtype': 'float64' }; var x = discreteUniform( 10, 0, 5, opts ); var p = uniform( 10, 0.0, 1.0, opts );

logEachMap( 'x: %d, p: %0.4f, P( X = x; p ): %0.4f', x, p, pmf );`

*

`C APIs`

`$3`

`c #include "stdlib/stats/base/dists/geometric/pmf.h"`

#### stdlib_base_dists_geometric_pmf( x, p )

Evaluates the [probability mass function][pmf] (PMF) of a [geometric][geometric-distribution] distribution with success probability 0 <= p <= 1.

`c double out = stdlib_base_dists_geometric_pmf( 4.0, 0.3 ); // returns ~0.072`

The function accepts the following arguments:

- x: [in] doubleinput value. - p:[in] double probability of success.

`c double stdlib_base_dists_geometric_pmf( const double x, const double p );`

`$3`

`c #include "stdlib/stats/base/dists/geometric/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 x; double p; double y; int i;

for ( i = 0; i < 25; i++ ) { x = stdlib_base_round( random_uniform( 0, 40 ) ); p = random_uniform( 0.0, 1.0 ); y = stdlib_base_dists_geometric_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`

[npm-image]: http://img.shields.io/npm/v/@stdlib/stats-base-dists-geometric-pmf.svg [npm-url]: https://npmjs.org/package/@stdlib/stats-base-dists-geometric-pmf

[test-image]: https://github.com/stdlib-js/stats-base-dists-geometric-pmf/actions/workflows/test.yml/badge.svg?branch=v0.3.1 [test-url]: https://github.com/stdlib-js/stats-base-dists-geometric-pmf/actions/workflows/test.yml?query=branch:v0.3.1

[coverage-image]: https://img.shields.io/codecov/c/github/stdlib-js/stats-base-dists-geometric-pmf/main.svg [coverage-url]: https://codecov.io/github/stdlib-js/stats-base-dists-geometric-pmf?branch=main

[chat-image]: https://img.shields.io/badge/zulip-join_chat-brightgreen.svg [chat-url]: https://stdlib.zulipchat.com

[stdlib]: https://github.com/stdlib-js/stdlib

[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors

[umd]: https://github.com/umdjs/umd [es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules

[deno-url]: https://github.com/stdlib-js/stats-base-dists-geometric-pmf/tree/deno [deno-readme]: https://github.com/stdlib-js/stats-base-dists-geometric-pmf/blob/deno/README.md [umd-url]: https://github.com/stdlib-js/stats-base-dists-geometric-pmf/tree/umd [umd-readme]: https://github.com/stdlib-js/stats-base-dists-geometric-pmf/blob/umd/README.md [esm-url]: https://github.com/stdlib-js/stats-base-dists-geometric-pmf/tree/esm [esm-readme]: https://github.com/stdlib-js/stats-base-dists-geometric-pmf/blob/esm/README.md [branches-url]: https://github.com/stdlib-js/stats-base-dists-geometric-pmf/blob/main/branches.md

[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/stats-base-dists-geometric-pmf/main/LICENSE

[geometric-distribution]: https://en.wikipedia.org/wiki/Geometric_distribution

[pmf]: https://en.wikipedia.org/wiki/Probability_mass_function


  
    About stdlib...
  

  We believe in a future in which the web is a preferred environment for numerical computation. To help realize this future, we've built stdlib. stdlib is a standard library, with an emphasis on numerical and scientific computation, written in JavaScript (and C) for execution in browsers and in Node.js.

  The library is fully decomposable, being architected in such a way that you can swap out and mix and match APIs and functionality to cater to your exact preferences and use cases.

  When you use stdlib, you can be absolutely certain that you are using the most thorough, rigorous, well-written, studied, documented, tested, measured, and high-quality code out there.

  To join us in bringing numerical computing to the web, get started by checking us out on GitHub, and please consider financially supporting stdlib. We greatly appreciate your continued support!

`Probability Mass Function`

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url]

> [Geometric][geometric-distribution] distribution [probability mass function][pmf] (PMF).

The [probability mass function][pmf] (PMF) for a [geometric][geometric-distribution] random variable is defined as

where 0 <= p <= 1 is the success probability. The random variable X denotes the number of failures until the first success in a sequence of independent Bernoulli trials.

`Installation`

``bash npm install @stdlib/stats-base-dists-geometric-pmf `

`Usage`

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

#### pmf( x, p )

Evaluates the [probability mass function][pmf] (PMF) of a [geometric][geometric-distribution] distribution with success probability 0 <= p <= 1.

`javascript var y = pmf( 4.0, 0.3 ); // returns ~0.072

y = pmf( 2.0, 0.7 ); // returns ~0.063

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( 2.0, -1.0 ); // returns NaN

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

#### pmf.factory( p )

Returns a function for evaluating the [probability mass function][pmf] (PMF) of a [geometric][geometric-distribution] distribution with success probability 0 <= p <= 1.

`javascript var mypmf = pmf.factory( 0.5 ); var y = mypmf( 3.0 ); // returns 0.0625

y = mypmf( 1.0 ); // returns 0.25 `

`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-geometric-pmf' );

var opts = { 'dtype': 'float64' }; var x = discreteUniform( 10, 0, 5, opts ); var p = uniform( 10, 0.0, 1.0, opts );

logEachMap( 'x: %d, p: %0.4f, P( X = x; p ): %0.4f', x, p, pmf ); `

*

`C APIs`

`$3`

`c #include "stdlib/stats/base/dists/geometric/pmf.h" `

#### stdlib_base_dists_geometric_pmf( x, p )

Evaluates the [probability mass function][pmf] (PMF) of a [geometric][geometric-distribution] distribution with success probability 0 <= p <= 1.

`c double out = stdlib_base_dists_geometric_pmf( 4.0, 0.3 ); // returns ~0.072 `

The function accepts the following arguments:

- x: [in] double input value. - p: [in] double probability of success.

`c double stdlib_base_dists_geometric_pmf( const double x, const double p ); `

`$3`

`c #include "stdlib/stats/base/dists/geometric/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 x; double p; double y; int i;

for ( i = 0; i < 25; i++ ) { x = stdlib_base_round( random_uniform( 0, 40 ) ); p = random_uniform( 0.0, 1.0 ); y = stdlib_base_dists_geometric_pmf( x, p ); printf( "x: %lf, p: %lf, P(X=x;p): %lf\n", x, p, y ); } } `

*

`Notice`

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`

[npm-image]: http://img.shields.io/npm/v/@stdlib/stats-base-dists-geometric-pmf.svg [npm-url]: https://npmjs.org/package/@stdlib/stats-base-dists-geometric-pmf

[chat-image]: https://img.shields.io/badge/zulip-join_chat-brightgreen.svg [chat-url]: https://stdlib.zulipchat.com

[stdlib]: https://github.com/stdlib-js/stdlib

[stdlib-authors]: https://github.com/stdlib-js/stdlib/graphs/contributors

[umd]: https://github.com/umdjs/umd [es-module]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Modules

[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/stats-base-dists-geometric-pmf/main/LICENSE

[geometric-distribution]: https://en.wikipedia.org/wiki/Geometric_distribution

[pmf]: https://en.wikipedia.org/wiki/Probability_mass_function