JSPM

  • ESM via JSPM
  • ES Module Entrypoint
  • Export Map
  • Keywords
  • License
  • Repository URL
  • TypeScript Types
  • README
  • Created
  • Published
  • Downloads 27201
  • Score
    100M100P100Q164300F
  • License MIT

A JavaScript model of a Gaussian distribution

Package Exports

  • gaussian
  • gaussian/lib/gaussian.js

This package does not declare an exports field, so the exports above have been automatically detected and optimized by JSPM instead. If any package subpath is missing, it is recommended to post an issue to the original package (gaussian) to support the "exports" field. If that is not possible, create a JSPM override to customize the exports field for this package.

Readme

Version Tests Coverage Status Downloads

gaussian

A JavaScript model of the Normal (or Gaussian) distribution.

API

Creating a Distribution

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.