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@stdlib/stats-base-dists-rayleigh

v0.2.2

Published

Rayleigh distribution.

Downloads

259

Readme

Rayleigh

NPM version Build Status Coverage Status

Rayleigh distribution.

Installation

npm install @stdlib/stats-base-dists-rayleigh

Usage

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

rayleigh

Rayleigh distribution.

var dist = rayleigh;
// returns {...}

The namespace contains the following distribution functions:

The namespace contains the following functions for calculating distribution properties:

The namespace contains a constructor function for creating a Rayleigh distribution object.

var Rayleigh = require( '@stdlib/stats-base-dists-rayleigh' ).Rayleigh;

var dist = new Rayleigh( 2.0 );

var y = dist.pdf( 0.8 );
// returns ~0.185

Examples

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

/*
* The Rayleigh distribution can be used to model wind speeds.
* Let's consider a scenario where we want to estimate various properties related to wind speeds.
*/

// Set the Rayleigh distribution parameter (scale parameter):
var s = 10.0;

// Calculate mean, variance, and standard deviation of the Rayleigh distribution:
console.log( rayleigh.mean( s ) );
// => ~12.533

console.log( rayleigh.variance( s ) );
// => ~42.920

console.log( rayleigh.stdev( s ) );
// => ~6.551

// Evaluate the Probability Density Function (PDF) for a specific wind speed:
var w = 15.0;
console.log( rayleigh.pdf( w, s ) );
// => ~0.049

// Determine Cumulative Distribution Function (CDF) for wind speeds up to a certain value:
var t = 15.0;
console.log( rayleigh.cdf( t, s ) );
// => ~0.675

// Calculate the probability of wind speeds exceeding the threshold:
var p = 1.0 - rayleigh.cdf( t, s );
console.log( 'Probability of wind speeds exceeding ' + t + ' m/s:', p );

// Find the wind speed at which there's a 70% chance it won't exceed using the Quantile function:
var c = 0.7;
console.log( rayleigh.quantile( c, s ) );
// => ~15.518

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.

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License

See LICENSE.

Copyright

Copyright © 2016-2024. The Stdlib Authors.