Abstract
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.
Highlights
Statistical inference is a key element in most fields of scientific research, with the goal of gaining knowledge about models from observed data
The Bayesian Analysis Toolkit (BAT) [1] is a software package providing a collection of algorithms and methods for performing Bayesian inference, focusing on the use of Markov Chain Monte Carlo (MCMC) techniques
Bayesian inference is a powerful technique for data analysis
Summary
Statistical inference is a key element in most fields of scientific research, with the goal of gaining knowledge about models from observed data. The Bayesian Analysis Toolkit (BAT) [1] is a software package providing a collection of algorithms and methods for performing Bayesian inference, focusing on the use of MCMC techniques It offers the infrastructure for implementing user-defined problems in a general-purpose language, allowing to specify likelihoods and prior distributions of arbitrary complexity without requiring the use of a tool-specific modeling language. In BAT.jl, this approach will become feasible as the AHMI algorithm permits to calculate the integrals in each of the subspaces, providing a proper reweighting when joining the samples of all individual chains With these developments in progress and further to come, BAT.jl is going to be a performant toolkit that offers a variety of state-of-the-art algorithms for Bayesian inference, allowing the users to choose the approach that fits their problems best. # activate BAT.jl (and other useful packages) using BAT using IntervalSets, Distributions, Plots
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