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gaussian

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A JavaScript model of a Gaussian distribution

github.com/errcw/gaussian

errcw/gaussian

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[![Version](https://img.shields.io/npm/v/gaussian)](https://www.npmjs.com/package/gaussian) [![Tests](https://github.com/errcw/gaussian/workflows/tests/badge.svg)](https://github.com/errcw/gaussian/actions/workflows/tests.yml) [![Coverage Status](https://coveralls.io/repos/github/errcw/gaussian/badge.svg?branch=master)](https://coveralls.io/github/errcw/gaussian?branch=master) [![Downloads](https://img.shields.io/npm/dy/gaussian)](https://www.npmjs.com/package/gaussian) # gaussian A JavaScript model of the [Normal](http://en.wikipedia.org/wiki/Normal_distribution) (or Gaussian) distribution. ## API ### Creating a Distribution ```javascript var gaussian = require('gaussian'); var distribution = gaussian(mean, variance); // Take a random sample using inverse transform sampling method. var sample = distribution.ppf(Math.random()); ``` ### Properties - `mean`: the mean (μ) of the distribution - `variance`: the variance (σ^2) of the distribution - `standardDeviation`: the standard deviation (σ) of the distribution ### Probability Functions - `pdf(x)`: the probability density function, which describes the probability of a random variable taking on the value _x_ - `cdf(x)`: the cumulative distribution function, which describes the probability of a random variable falling in the interval (−∞, _x_] - `ppf(x)`: the percent point function, the inverse of _cdf_ ### Combination Functions - `mul(d)`: returns the product distribution of this and the given distribution; equivalent to `scale(d)` when d is a constant - `div(d)`: returns the quotient distribution of this and the given distribution; equivalent to `scale(1/d)` when d is a constant - `add(d)`: returns the result of adding this and the given distribution's means and variances - `sub(d)`: returns the result of subtracting this and the given distribution's means and variances - `scale(c)`: returns the result of scaling this distribution by the given constant ### Generation Function - `random(n)`: returns an array of generated `n` random samples correspoding to the Gaussian parameters.