BAT.jl: A Julia-Based Tool for Bayesian Inference
O. Schulz, Frederik Beaujean, Allen Christopher Caldwell, Cornelius Grunwald, Vasyl Hafych, K. Kroeninger, Salvatore La Cagnina, Lars Röhrig, L. Shtembari · SN Computer Science · 2021
Abstract We describe the development of a multi-purpose software for Bayesian statistical inference, BAT.jl, written in the Julia language. The major design considerations and implemented algorithms are summarized here, together with a test suite that ensures the proper functioning of the algorithms. We also give an extended example from the realm of physics that demonstrates the functionalities of BAT.jl.