Metadata-Version: 2.1
Name: inference-tools
Version: 0.5.4
Summary: A collection of python tools for Bayesian data analysis
Home-page: https://github.com/C-bowman/inference-tools
Author: Chris Bowman
Author-email: chris.bowman.physics@gmail.com
License: UNKNOWN
Description: # inference-tools
        
        [![Documentation Status](https://readthedocs.org/projects/inference-tools/badge/?version=stable)](https://inference-tools.readthedocs.io/en/stable/?badge=stable)
        [![GitHub license](https://img.shields.io/github/license/C-bowman/inference-tools?color=blue)](https://github.com/C-bowman/inference-tools/blob/master/LICENSE)
        [![PyPI - Downloads](https://img.shields.io/pypi/dm/inference-tools?color=purple)](https://pypi.org/project/inference-tools/)
        ![PyPI - Python Version](https://img.shields.io/pypi/pyversions/inference-tools)
        
        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-docs/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-docs/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-docs/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-docs/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-docs/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-docs/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/).
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
