BAT.jl Upgrading the Bayesian Analysis Toolkit
Allen Christopher Caldwell, Cornelius Grunwald, Vasyl Hafych, Kevin Kröninger, Salvatore La Cagnina, O. Schulz, L. Shtembari · EPJ Web of Conferences · 2020
In all but the simplest cases, performing data analysis based on Bayesian reasoning requires the use of advanced algorithms. The Bayesian Analysis Toolkit (BAT) provides a collection of algorithms and methods that facilitate the application of Bayesian statistics to user-defined problems of arbitrary complexity. With BAT.jl, we present a modern rewrite of BAT in the Julia programming language. Through the use of a modular software design that is capable of running parallel and distributed, and by extending the tool with new sampling and integration algorithms, BAT.jl is a high-performance framework for Bayesian inference, meeting the requirements of modern data analysis.