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https://github.com/tpapp/LogDensityProblems.jl

A common framework for implementing and using log densities for inference.
https://github.com/tpapp/LogDensityProblems.jl

bayesian bayesian-data-analysis bayesian-inference bayesian-methods julia mcmc

Last synced: 27 days ago
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A common framework for implementing and using log densities for inference.

Host: GitHub
URL: https://github.com/tpapp/LogDensityProblems.jl
Owner: tpapp
License: other
Created: 2018-07-11T14:33:58.000Z (almost 6 years ago)
Default Branch: master
Last Pushed: 2023-07-19T02:00:34.000Z (11 months ago)
Last Synced: 2024-05-14T11:06:34.284Z (about 1 month ago)
Topics: bayesian, bayesian-data-analysis, bayesian-inference, bayesian-methods, julia, mcmc
Language: Julia
Size: 737 KB
Stars: 39
Watchers: 5
Forks: 9
Open Issues: 6
Metadata Files:
- Readme: README.md
- Changelog: CHANGELOG.md
- License: LICENSE.md

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awesome-stars - tpapp/LogDensityProblems.jl - A common framework for implementing and using log densities for inference. (Julia)

README

        # LogDensityProblems.jl

![Lifecycle](https://img.shields.io/badge/lifecycle-experimental-orange.svg)

![lifecycle](https://img.shields.io/badge/lifecycle-maturing-blue.svg)

[![build](https://github.com/tpapp/LogDensityProblems.jl/workflows/CI/badge.svg)](https://github.com/tpapp/LogDensityProblems.jl/actions?query=workflow%3ACI)

[![codecov.io](http://codecov.io/github/tpapp/LogDensityProblems.jl/coverage.svg?branch=master)](http://codecov.io/github/tpapp/LogDensityProblems.jl?branch=master)

[![Documentation](https://img.shields.io/badge/docs-stable-blue.svg)](https://tpapp.github.io/LogDensityProblems.jl/stable)

[![Documentation](https://img.shields.io/badge/docs-master-blue.svg)](https://tpapp.github.io/LogDensityProblems.jl/dev)

A common framework for implementing and using log densities for inference, providing the following functionality.

1. The [`logdensity`](https://tamaspapp.eu/LogDensityProblems.jl/dev/#LogDensityProblems.logdensity) method with corresponding interface, which can be used by other packages that operate on (log) densities and need to evaluate the log densities or the gradients (eg [MCMC](https://en.wikipedia.org/wiki/Markov_chain_Monte_Carlo), [MAP](https://en.wikipedia.org/wiki/Maximum_a_posteriori_estimation), [ML](https://en.wikipedia.org/wiki/Maximum_likelihood_estimation) or similar methods).

2. Various utility functions for debugging and testing log densities.

**NOTE** As of version 1.0, transformed log densities have been moved to [TransformedLogDensities.jl](https://github.com/tpapp/TransformedLogDensities.jl). Existing code that uses `TransformedLogDensity` should add

```

using TransformedLogDensities

```

or equivalent.

**NOTE**: As of version 2.0, automatic differentiation backends have been moved to [https://github.com/tpapp/LogDensityProblemsAD.jl](https://github.com/tpapp/LogDensityProblemsAD.jl "LogDensityProblemsAD.jl"). If your code uses `ADgradient`, simply add

```julia

using LogDensityProblemsAD

```

or equivalent.

See the [documentation](https://tpapp.github.io/LogDensityProblems.jl/dev) for details.