Speaker
Dr
Daniel Kollar
(Max-Planck-Institut fuer Physik, Munich)
Description
The main goals of data analysis are to infer the parameters of
models from data, to draw conclusions on the validity of models,
and to compare their predictions allowing to select the most
appropriate model.
The Bayesian Analysis Toolkit, BAT, is a tool developed to evaluate
the posterior probability distribution for models and their
parameters. It is centered around Bayes' Theorem and is realized
with the use of Markov Chain Monte Carlo giving access to the full
posterior probability distribution. This enables straightforward
parameter estimation, limit setting and uncertainty propagation.
BAT is implemented in C++ and allows a flexible definition of models.
It is interfaced to other software packaged commonly used in
high-energy physics: ROOT, Minuit, RooStats and CUBA. A set of
predefined models exists to cover standard statistical cases.
We will present an overview of the software and the algorithms
implemented. Recent updates and future plans will be summarized.
Authors
Allen Caldwell
(Max Planck Institute)
Dr
Daniel Kollar
(Max-Planck-Institut fuer Physik, Munich)
Frederik Beaujean
(Max Planck Institute for Physics)
Kevin Alexander Kroeninger
(Georg-August-Universitaet Goettingen (DE))
Shabnaz Pashapouralamdari
(Georg-August-Universitaet Goettingen (DE))
