<commit_before>Probability Mass Function
===

> [Hypergeometric][hypergeometric] distribution [probability mass function][pmf] (PMF).

<!-- <intro> -->

Imagine a scenario with an urn holding black and white balls. Let `m` be the number of white balls in the urn and `n` be the number of black balls. We draw `k` balls from the urn. Defining the random variable `X` as the number of white balls drawn in total, `X` is said to follow a [hypergeometric distribution][hypergeometric].
The [probability mass function][pmf] (PMF) for a [hypergeometric][hypergeometric] random variable is given by

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

<div class="equation" align="center" data-raw-text="f(x;m,n,k)=P(X=x;m,n,k)=\begin{cases} {{{m \choose x} {n \choose {k-x}}}\over {{m+n} \choose k}} & \text{ for } x = 0,1,2,\ldots \\
0 & \text{ otherwise}
\end{cases}" data-equation="eq:pmf">
    <img src="" alt="Probability mass function (PMF) for a hypergeometric distribution.">
    <br>
</div>

<!-- </equation> -->

<!-- </intro> -->

<!-- <usage> -->

## Usage
``` javascript
var pmf = require( '@stdlib/math/base/dist/hypergeometric/pmf' );
```

#### pmf( x, m, n, k )

Evaluates the [probability mass function][pmf] (PMF) for a [hypergeometric ][hypergeometric] distribution with parameters `m`, `n` and `k`, where
`m` is the number of white balls in the urn, `n` is the number of black balls in the urn and `k` is the number of draws.<commit_msg>Escape equation ampersands<commit_after>Probability Mass Function
===

> [Hypergeometric][hypergeometric] distribution [probability mass function][pmf] (PMF).

<!-- <intro> -->

Imagine a scenario with an urn holding black and white balls. Let `m` be the number of white balls in the urn and `n` be the number of black balls. We draw `k` balls from the urn. Defining the random variable `X` as the number of white balls drawn in total, `X` is said to follow a [hypergeometric distribution][hypergeometric].
The [probability mass function][pmf] (PMF) for a [hypergeometric][hypergeometric] random variable is given by

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

<div class="equation" align="center" data-raw-text="f(x;m,n,k)=P(X=x;m,n,k)=\begin{cases} {{{m \choose x} {n \choose {k-x}}}\over {{m+n} \choose k}} &amp; \text{ for } x = 0,1,2,\ldots \\
0 &amp; \text{ otherwise}
\end{cases}" data-equation="eq:pmf">
    <img src="" alt="Probability mass function (PMF) for a hypergeometric distribution.">
    <br>
</div>

<!-- </equation> -->

<!-- </intro> -->

<!-- <usage> -->

## Usage
``` javascript
var pmf = require( '@stdlib/math/base/dist/hypergeometric/pmf' );
```

#### pmf( x, m, n, k )

Evaluates the [probability mass function][pmf] (PMF) for a [hypergeometric ][hypergeometric] distribution with parameters `m`, `n` and `k`, where
`m` is the number of white balls in the urn, `n` is the number of black balls in the urn and `k` is the number of draws.
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