The main goals of a typical data anaysis are to compare model predictions with data, draw conclusions on the validity of a model as a representation of the data, and to extract the values of the free parameters of a model. The Bayesian Analysis Toolkit, BAT, is a software package which addresses the points above. It is designed to help solve statistical problems encountered in Bayesian inference. BAT 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 and enables straightforward parameter estimation, limit setting and uncertainty propagation. BAT is implemented in C++ and allows for a flexible definition of mathematical models and applications while keeping in mind the reliability and speed requirements of the numerical operations. It provides a set of algorithms for numerical integration, optimization and error propagation. Predefined models exist for standard cases. In addition, methods to judge the goodness-of-fit of a model are implemented. An interface to ROOT allows for further analysis and graphical display of results. BAT can also be run from within RooStats analysis.
Keywords for this software
References in zbMATH (referenced in 7 articles , 1 standard article )
Showing results 1 to 7 of 7.
- Oliver Schulz, Frederik Beaujean, Allen Caldwell, Cornelius Grunwald, Vasyl Hafych, Kevin Kröninger, Salvatore La Cagnina, Lars Röhrig, Lolian Shtembari: BAT.jl - A Julia-based tool for Bayesian inference (2020) arXiv
- Casadei, Diego; Grunwald, Cornelius; Kröninger, Kevin; Mentzel, Florian: Objective Bayesian analysis of counting experiments with correlated sources of background (2018)
- Ghosh, Diptimoy; Salvarezza, Matteo; Senia, Fabrizio: Extending the analysis of electroweak precision constraints in composite Higgs models (2017)
- Cacchio, Vincenzo; Chowdhury, Debtosh; Eberhardt, Otto; Murphy, Christopher W.: Next-to-leading order unitarity fits in two-Higgs-doublet models with soft (\mathbbZ_2) breaking (2016)
- de Blas, J.; Ciuchini, M.; Franco, E.; Mishima, S.; Pierini, M.; Reina, L.; Silvestrini, L.: Electroweak precision observables and Higgs-boson signal strengths in the standard model and beyond: present and future (2016)
- Beaujean, Frederik; Caldwell, Allen: A test statistic for weighted runs (2011)
- Caldwell, Allen; Kollár, Daniel; Kröninger, Kevin: BAT - the Bayesian analysis toolkit (2009)