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# Logarithm of Probability Density Function

> [Beta][beta-distribution] distribution logarithm of probability density function (PDF).

<section class="intro">

The [probability density function][pdf] (PDF) for a [beta][beta-distribution] random variable is

<!-- <equation class="equation" label="eq:beta_pdf" align="center" raw="f(x;\alpha,\beta)= \begin{cases} \frac{\Gamma(\alpha + \beta)}{\Gamma(\alpha) + \Gamma(\beta)}{x^{\alpha-1}(1-x)^{\beta-1}} & \text{ for } x \in (0,1) \\ 0 & \text{ otherwise } \end{cases}" alt="Probability density function (PDF) for a beta distribution."> -->

<div class="equation" align="center" data-raw-text="f(x;\alpha,\beta)= \begin{cases} \frac{\Gamma(\alpha + \beta)}{\Gamma(\alpha) + \Gamma(\beta)}{x^{\alpha-1}(1-x)^{\beta-1}} &amp; \text{ for } x \in (0,1) \\ 0 &amp; \text{ otherwise } \end{cases}" data-equation="eq:beta_pdf">
    <img src="https://cdn.jsdelivr.net/gh/stdlib-js/stdlib@51534079fef45e990850102147e8945fb023d1d0/lib/node_modules/@stdlib/stats/base/dists/beta/logpdf/docs/img/equation_beta_pdf.svg" alt="Probability density function (PDF) for a beta distribution.">
    <br>
</div>

<!-- </equation> -->

where `alpha > 0` is the first shape parameter and `beta > 0` is the second shape parameter.

</section>

<!-- /.intro -->

<section class="usage">

## Usage

```javascript
var logpdf = require( '@stdlib/stats/base/dists/beta/logpdf' );
```

#### logpdf( x, alpha, beta )

Evaluates the natural logarithm of the [probability density function][pdf] (PDF) for a [beta][beta-distribution]  distribution with parameters `alpha` (first shape parameter) and `beta` (second shape parameter).

```javascript
var y = logpdf( 0.5, 0.5, 1.0 );
// returns ~-0.347

y = logpdf( 0.1, 1.0, 1.0 );
// returns 0.0

y = logpdf( 0.8, 4.0, 2.0 );
// returns ~0.717
```

If provided an input value `x` outside the support `[0,1]`, the function returns `-Infinity`.

```javascript
var y = logpdf( -0.1, 1.0, 1.0 );
// returns -Infinity

y = logpdf( 1.1, 1.0, 1.0 );
// returns -Infinity
```

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

```javascript
var y = logpdf( NaN, 1.0, 1.0 );
// returns NaN

y = logpdf( 0.0, NaN, 1.0 );
// returns NaN

y = logpdf( 0.0, 1.0, NaN );
// returns NaN
```

If provided `alpha <= 0`, the function returns `NaN`.

```javascript
var y = logpdf( 0.5, 0.0, 1.0 );
// returns NaN

y = logpdf( 0.5, -1.0, 1.0 );
// returns NaN
```

If provided `beta <= 0`, the function returns `NaN`.

```javascript
var y = logpdf( 0.5, 1.0, 0.0 );
// returns NaN

y = logpdf( 0.5, 1.0, -1.0 );
// returns NaN
```

#### logpdf.factory( alpha, beta )

Returns a `function` for evaluating the natural logarithm of the [PDF][pdf] for a [beta][beta-distribution]  distribution with parameters `alpha` (first shape parameter) and `beta` (second shape parameter).

```javascript
var mylogPDF = logpdf.factory( 0.5, 0.5 );

var y = mylogPDF( 0.8 );
// returns ~-0.228

y = mylogPDF( 0.3 );
// returns ~-0.364
```

</section>

<!-- /.usage -->

<section class="notes">

## Notes

-   In virtually all cases, using the `logpdf` or `logcdf` functions is preferable to manually computing the logarithm of the `pdf` or `cdf`, respectively, since the latter is prone to overflow and underflow.

</section>

<!-- /.notes -->

<section class="examples">

## Examples

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

```javascript
var randu = require( '@stdlib/random/base/randu' );
var EPS = require( '@stdlib/constants/float64/eps' );
var logpdf = require( '@stdlib/stats/base/dists/beta/logpdf' );

var alpha;
var beta;
var x;
var y;
var i;

for ( i = 0; i < 10; i++ ) {
    x = randu();
    alpha = ( randu()*5.0 ) + EPS;
    beta = ( randu()*5.0 ) + EPS;
    y = logpdf( x, alpha, beta );
    console.log( 'x: %d, α: %d, β: %d, ln(f(x;α,β)): %d', x.toFixed( 4 ), alpha.toFixed( 4 ), beta.toFixed( 4 ), y.toFixed( 4 ) );
}
```

</section>

<!-- /.examples -->

<section class="links">

[beta-distribution]: https://en.wikipedia.org/wiki/Beta_distribution

[pdf]: https://en.wikipedia.org/wiki/Probability_density_function

</section>

<!-- /.links -->
