BAT - The Bayesian Analysis Toolkit

TL;DR

The paper introduces BAT, a Bayesian data analysis toolkit utilizing Markov Chain Monte Carlo to provide comprehensive posterior distributions, simplifying parameter estimation, limit setting, and uncertainty propagation.

Contribution

It presents a new Bayesian analysis toolkit that integrates MCMC methods for full posterior exploration, with practical features for goodness-of-fit assessment.

Findings

Provides full posterior distributions for parameters

Simplifies limit setting and uncertainty propagation

Includes an intuitive goodness-of-fit criterion

Abstract

We describe the development of a new toolkit for data analysis. The analysis package is based on Bayes' Theorem, and is realized with the use of Markov Chain Monte Carlo. This gives access to the full posterior probability distribution. Parameter estimation, limit setting and uncertainty propagation are implemented in a straightforward manner. A goodness-of-fit criterion is presented which is intuitive and of great practical use.