https://github.com/e-dard/Godist

Probability distributions and associated methods in Go
https://github.com/e-dard/Godist

Last synced: 20 days ago
JSON representation

Probability distributions and associated methods in Go

• Host: GitHub
• URL: https://github.com/e-dard/Godist
• Owner: e-dard
• License: mit
• Created: 2014-09-05T09:48:51.000Z (about 10 years ago)
• Default Branch: master
• Last Pushed: 2015-05-11T10:38:48.000Z (over 9 years ago)
• Last Synced: 2024-10-14T21:47:16.115Z (30 days ago)
• Language: Go
• Size: 195 KB
• Stars: 38
• Watchers: 6
• Forks: 7
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
godist

======

[![Build Status](https://drone.io/github.com/e-dard/godist/status.png)](https://drone.io/github.com/e-dard/godist/latest)

[![GoDoc](https://godoc.org/github.com/e-dard/godist?status.svg)](http://godoc.org/github.com/e-dard/godist)

`godist` provides some Go implementations of useful continuous and

discrete probability distributions, as well as some handy methods for

working with them.

The general idea is that I will add to these over time, but that each

distribution will implement the following interface:

```go

type Distribution interface{

	// distribution mean

	Mean() (float64, error)

	// distribution median

	Median() (float64, error)

	// distribution mode

	Mode() (float64, error)

	// distribution variance

	Variance() (float64, error)

	// generate a random value according to the probability distribution

	Float64() (float64, error)

}

```

In practice, distributions may also provide other useful methods, where

appropriate.

The intentions of `godist` is not to provide the fastest, most efficient

implementations, but instead to provide idiomatic Go implementations

that can be easily understood and extended. Having said that, where

there are useful and well-understood numerical tricks and tools to

improve performance, these have been utilised and documented.

Contributions welcome!

### Current Distributions

- Beta Distribution

- Empirical Distribution