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 3 articles , 1 standard article )
Showing results 1 to 3 of 3.
- Ghosh, Diptimoy; Salvarezza, Matteo; Senia, Fabrizio: Extending the analysis of electroweak precision constraints in composite Higgs models (2017)
- Beaujean, Frederik; Caldwell, Allen: A test statistic for weighted runs (2011)
- Caldwell, Allen; Kollár, Daniel; Kröninger, Kevin: BAT - the Bayesian analysis toolkit (2009)