stdlib-js
<summary>
About stdlib...
</summary>
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 Density Function
[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url]
[Normal][normal-distribution] distribution probability density function (PDF).
µ is the mean and σ is the standard deviation.
bash
npm install @stdlib/stats-base-dists-normal-pdf
javascript
var pdf = require( '@stdlib/stats-base-dists-normal-pdf' );
#### pdf( x, mu, sigma )
Evaluates the [probability density function][pdf] (PDF) for a [normal][normal-distribution] distribution with parameters mu (mean) and sigma (standard deviation).
javascript
var y = pdf( 2.0, 0.0, 1.0 );
// returns ~0.054
y = pdf( -1.0, 4.0, 2.0 );
// returns ~0.009
If provided NaN as any argument, the function returns NaN.
javascript
var y = pdf( NaN, 0.0, 1.0 );
// returns NaN
y = pdf( 0.0, NaN, 1.0 );
// returns NaN
y = pdf( 0.0, 0.0, NaN );
// returns NaN
If provided sigma < 0, the function returns NaN.
javascript
var y = pdf( 2.0, 0.0, -1.0 );
// returns NaN
If provided sigma = 0, the function evaluates the [PDF][pdf] of a [degenerate distribution][degenerate-distribution] centered at mu.
javascript
var y = pdf( 2.0, 8.0, 0.0 );
// returns 0.0
y = pdf( 8.0, 8.0, 0.0 );
// returns Infinity
#### pdf.factory( mu, sigma )
Partially apply mu and sigma to create a reusable function for evaluating the PDF.
javascript
var mypdf = pdf.factory( 10.0, 2.0 );
var y = mypdf( 10.0 );
// returns ~0.199
y = mypdf( 5.0 );
// returns ~0.009
javascript
var randu = require( '@stdlib/random-base-randu' );
var pdf = require( '@stdlib/stats-base-dists-normal-pdf' );
var sigma;
var mu;
var x;
var y;
var i;
for ( i = 0; i < 10; i++ ) {
x = randu() * 10.0;
mu = (randu() * 10.0) - 5.0;
sigma = randu() * 20.0;
y = pdf( x, mu, sigma );
console.log( 'x: %d, µ: %d, σ: %d, f(x;µ,σ): %d', x, mu, sigma, y );
}
