@stdlib/stats-base-dists-normal-logpdf NPM

Logarithm of Probability Density Function

Evaluate the natural logarithm of the probability density function (PDF) for a normal distribution.

The probability density function (PDF) for a normal random variable is

where µ is the mean and σ is the standard deviation.

Installation

npm install @stdlib/stats-base-dists-normal-logpdf

Usage

var logpdf = require( '@stdlib/stats-base-dists-normal-logpdf' );

logpdf( x, mu, sigma )

Evaluates the natural logarithm of the probability density function (PDF) for a normal distribution with parameters mu (mean) and sigma (standard deviation).

var y = logpdf( 2.0, 0.0, 1.0 );
// returns ~-2.919

y = logpdf( -1.0, 4.0, 2.0 );
// returns ~-4.737

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

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

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

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

If provided sigma < 0, the function returns NaN.

var y = logpdf( 2.0, 0.0, -1.0 );
// returns NaN

If provided sigma = 0, the function evaluates the natural logarithm of the PDF of a degenerate distribution centered at mu.

var y = logpdf( 2.0, 8.0, 0.0 );
// returns -Infinity

y = logpdf( 8.0, 8.0, 0.0 );
// returns Infinity

logpdf.factory( mu, sigma )

Returns a function for evaluating the probability density function (PDF) of a normal distribution with parameters mu (mean) and sigma (standard deviation).

var mylogpdf = logpdf.factory( 10.0, 2.0 );

var y = mylogpdf( 10.0 );
// returns ~-1.612

y = mylogpdf( 5.0 );
// returns ~-4.737

Examples

var randu = require( '@stdlib/random-base-randu' );
var logpdf = require( '@stdlib/stats-base-dists-normal-logpdf' );

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 = logpdf( x, mu, sigma );
    console.log( 'x: %d, µ: %d, σ: %d, ln(f(x;µ,σ)): %d', x, mu, sigma, y );
}

Notice

This package is part of 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, see the main project repository.