Speaker
Daniel Kollár
(CERN)
Description
The main goals of a typical data anaysis are to compare model predictions with data, to draw conclusions on the validity of the model as a representation of the data, and to extract the possible values of parameters within the context of a model.
The Bayesian Analysis Toolkit, BAT, is a tool developed to evaluate the posterior probability distribution for models and their parameters. It 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.
The BAT is implemented in C++ and allows for flexible definition of mathematical models and applications. 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 inferface to ROOT allows for further analysis and graphical display of results.
BAT has been developed primarily in the context of data analysis for particle physics experiments. The applications so far range from the extraction of structure functions in ZEUS and the calculation of the sensitivity of GERDA to double beta-decay, to kinematic fitting of top-quark events in ATLAS. Applications in cosmology are also being investigated.
Primary authors
Allen Caldwell
(MPI for Physics Munich)
Daniel Kollár
(CERN)
Kevin Kröninger
(Universität Göttingen)
