https://github.com/bat/UltraNest.jl

Julia wrapper for UltraNest: advanced nested sampling for model comparison and parameter estimation
https://github.com/bat/UltraNest.jl

Last synced: 3 months ago
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Julia wrapper for UltraNest: advanced nested sampling for model comparison and parameter estimation

• Host: GitHub
• URL: https://github.com/bat/UltraNest.jl
• Owner: bat
• License: other
• Created: 2020-12-23T10:49:24.000Z (almost 4 years ago)
• Default Branch: main
• Last Pushed: 2022-03-14T10:08:44.000Z (over 2 years ago)
• Last Synced: 2024-07-10T19:16:06.425Z (4 months ago)
• Language: Julia
• Size: 398 KB
• Stars: 6
• Watchers: 4
• Forks: 0
• Open Issues: 1
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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• awesome-sciml - bat/UltraNest.jl: Julia wrapper for UltraNest: advanced nested sampling for model comparison and parameter estimation

README

        
# UltraNest.jl

[![Documentation for stable version](https://img.shields.io/badge/docs-stable-blue.svg)](https://bat.github.io/UltraNest.jl/stable)

[![Documentation for development version](https://img.shields.io/badge/docs-dev-blue.svg)](https://bat.github.io/UltraNest.jl/dev)

[![License](http://img.shields.io/badge/license-MIT-brightgreen.svg?style=flat)](LICENSE.md)

[![Build Status](https://github.com/bat/UltraNest.jl/workflows/CI/badge.svg?branch=main)](https://github.com/bat/UltraNest.jl/actions?query=workflow%3ACI)

[![Codecov](https://codecov.io/gh/bat/UltraNest.jl/branch/main/graph/badge.svg)](https://codecov.io/gh/bat/UltraNest.jl)

## Documentation

* [Documentation for stable version](https://bat.github.io/UltraNest.jl/stable)

* [Documentation for development version](https://bat.github.io/UltraNest.jl/dev)

This is a Julia wrapper for Python nested sampling package

[UltraNest](https://github.com/JohannesBuchner/UltraNest).

Nested sampling allows Bayesian inference on arbitrary user-defined likelihoods. In particular, posterior probability distributions on model parameters are constructed, and the marginal likelihood ("evidence") Z is computed. The former can be used to describe the parameter constraints of the data, the latter can be used for model comparison (via *Bayes factors*) as a measure of the prediction parsimony of a model.

UltraNest provides novel, advanced techniques (see [how it works](https://johannesbuchner.github.io/UltraNest/method.html)). They are especially remarkable for being free of tuning parameters and theoretically justified. Beyond that, UltraNest has support for Big Data sets and high-performance computing applications.