Metadata-Version: 2.4
Name: inference-tools
Version: 0.15.0
Summary: A collection of python tools for Bayesian data analysis
Author-email: Chris Bowman <chris.bowman.physics@gmail.com>
License: MIT License
        
        Copyright (c) 2018 Chris Bowman
        
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Project-URL: Homepage, https://github.com/C-bowman/inference-tools
Project-URL: Documentation, https://inference-tools.readthedocs.io/en/stable/
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.9
Description-Content-Type: text/markdown
License-File: LICENSE
Requires-Dist: numpy>=1.20
Requires-Dist: scipy>=1.6.3
Requires-Dist: matplotlib>=3.4.2
Provides-Extra: tests
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Requires-Dist: pyqt5>=5.15; extra == "tests"
Requires-Dist: hypothesis>=6.24; extra == "tests"
Requires-Dist: freezegun>=1.1.0; extra == "tests"
Dynamic: license-file

# inference-tools

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This package provides a set of Python-based tools for Bayesian data analysis
which are simple to use, allowing them to applied quickly and easily.

Inference-tools is not a framework for Bayesian modelling (e.g. like [PyMC](https://docs.pymc.io/)),
but instead provides tools to sample from user-defined models using MCMC, and to analyse and visualise
the sampling results.

## Features

 - Implementations of MCMC algorithms like Gibbs sampling and Hamiltonian Monte-Carlo for 
 sampling from user-defined posterior distributions.
 
 - Density estimation and plotting tools for analysing and visualising inference results.
 
 - Gaussian-process regression and optimisation.


| | | |
|:-------------------------:|:-------------------------:|:-------------------------:|
| [Gibbs Sampling](https://github.com/C-bowman/inference-tools/blob/master/demos/gibbs_sampling_demo.ipynb) <img width="1604" alt="1" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/gallery_images/gallery_gibbs_sampling.png"> | [Hamiltonian Monte-Carlo](https://github.com/C-bowman/inference-tools/blob/master/demos/hamiltonian_mcmc_demo.ipynb) <img width="1604" alt="2" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/gallery_images/gallery_hmc.png"> | [Density estimation](https://github.com/C-bowman/inference-tools/blob/master/demos/density_estimation_demo.ipynb) <img width="1604" alt="3" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/gallery_images/gallery_density_estimation.png"> |
| Matrix plotting <img width="1604" alt="4" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/getting_started_images/matrix_plot_example.png"> | Highest-density intervals <img width="1604" alt="5" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/gallery_images/gallery_hdi.png"> | [GP regression](https://github.com/C-bowman/inference-tools/blob/master/demos/gp_regression_demo.ipynb) <img width="1604" alt="6" src="https://raw.githubusercontent.com/C-bowman/inference-tools/master/docs/source/images/gallery_images/gallery_gpr.png"> |

## Installation

inference-tools is available from [PyPI](https://pypi.org/project/inference-tools/), 
so can be easily installed using [pip](https://pip.pypa.io/en/stable/) as follows:
```bash
pip install inference-tools
```

## Documentation

Full documentation is available at [inference-tools.readthedocs.io](https://inference-tools.readthedocs.io/en/stable/).
