@stdlib/stats/base/dists/binomial/logpmf/README.md

Standard library statistical functions.

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# Logarithm of Probability Mass Function

> Evaluate the natural logarithm of the probability mass function (PMF) for a [binomial][binomial-distribution] distribution.

<section class="intro">

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

<!-- <equation class="equation" label="eq:binomial_pmf" align="center" raw="f(x;n,p)=P(X=x;n,p)=\begin{cases} \textstyle {n \choose x}\, p^x (1-p)^{n-x} & \text{ for } x = 0,1,2,\ldots \\ 0 & \text{ otherwise} \end{cases}" alt="Probability mass function (PMF) for a binomial distribution."> -->

<div class="equation" align="center" data-raw-text="f(x;n,p)=P(X=x;n,p)=\begin{cases} \textstyle {n \choose x}\, p^x (1-p)^{n-x} &amp; \text{ for } x = 0,1,2,\ldots \\ 0 &amp; \text{ otherwise} \end{cases}" data-equation="eq:binomial_pmf">
    <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@51534079fef45e990850102147e8945fb023d1d0/lib/node_modules/@stdlib/stats/base/dists/binomial/logpmf/docs/img/equation_binomial_pmf.svg" alt="Probability mass function (PMF) for a binomial distribution.">
    <br>
</div>

<!-- </equation> -->

where `n` is the number of trials and `0 <= p <= 1` is the success probability.

</section>

<!-- /.intro -->

<section class="usage">

## Usage

```javascript
var logpmf = require( '@stdlib/stats/base/dists/binomial/logpmf' );
```

#### logpmf( x, n, p )

Evaluates the natural logarithm of the [probability mass function][pmf] (PMF) for a [binomial][binomial-distribution] distribution with number of trials `n` and success probability `p`.

```javascript
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 ~-5.108
```

If provided `NaN` as any argument, the function returns `NaN`.

```javascript
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 NaN
```

If provided a number of trials `n` which is not a nonnegative integer, the function returns `NaN`.

```javascript
var y = logpmf( 2.0, 1.5, 0.5 );
// returns NaN

y = logpmf( 2.0, -2.0, 0.5 );
// returns NaN
```

If provided a success probability `p` outside of `[0,1]`, the function returns `NaN`.

```javascript
var y = logpmf( 2.0, 20, -1.0 );
// returns NaN

y = logpmf( 2.0, 20, 1.5 );
// returns NaN
```

#### logpmf.factory( n, p )

Returns a function for evaluating the [probability mass function][pmf] (PMF) of a [binomial][binomial-distribution] distribution with number of trials `n` and success probability `p`.

```javascript
var mylogpmf = logpmf.factory( 10, 0.5 );

var y = mylogpmf( 3.0 );
// returns ~-2.144

y = mylogpmf( 5.0 );
// returns ~-1.402
```

</section>

<!-- /.usage -->

<section class="examples">

## Examples

<!-- eslint no-undef: "error" -->

```javascript
var randu = require( '@stdlib/random/base/randu' );
var round = require( '@stdlib/math/base/special/round' );
var logpmf = require( '@stdlib/stats/base/dists/binomial/logpmf' );

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 ) );
}
```

</section>

<!-- /.examples -->

<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->

<section class="related">

</section>

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<section class="links">

[binomial-distribution]: https://en.wikipedia.org/wiki/Binomial_distribution

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

</section>

<!-- /.links -->