BAT.jl -- A Julia-based tool for Bayesian inference
Oliver Schulz, Frederik Beaujean, Allen Caldwell, Cornelius Grunwald, Vasyl Hafych, Kevin Kröninger, Salvatore La Cagnina, Lars Röhrig, Lolian Shtembari
arXiv:2008.03132v1
Originality Synthesis-oriented
AI Analysis
This provides a new tool for researchers in fields like physics needing Bayesian analysis, but it is incremental as it adapts existing methods to a new programming language.
The authors developed BAT.jl, a multi-purpose Bayesian inference software in Julia, and demonstrated its functionality with a physics example.
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.