The inference-tools package
Introduction
This package aims to provide a set of python-based tools for Bayesian data analysis which are simple to use, allowing them to be applied quickly and easily.
Inference tools is not a framework for building Bayesian/probabilistic models - instead it provides tools to characterise arbitrary posterior distributions (given a function which maps model parameters to a log-probability) via MCMC sampling.
This type of ‘black-box’ functionality allows for inference without the requirement of first implementing the problem within a modelling framework.
Additionally, the package provides tools for analysing and plotting sampling results, as well as implementations of some useful applications of Gaussian processes.
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.
Installation
inference-tools is available from PyPI, and can be installed using pip:
pip install inference-tools
See Getting started for a worked example of using the package.
Getting involved
Requests for features/improvements can be made via the
issue tracker. If you have questions
or are interested in getting involved with the development of this package, please contact
me at chris.bowman.physics@gmail.com.
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