Speaker
Frederik Beaujean
(Max Planck Institute for Physics)
Description
The Bayesian Analysis Toolkit (BAT) is a C++ library designed to analyze data through
the application of Bayes' theorem.
For parameter inference, it is necessary to draw samples from the posterior distribution
within the given statistical model. At its core, BAT uses an adaptive Markov Chain Monte Carlo (MCMC) algorithm.
As an example of a challenging task, we consider the analysis of rare B-decays in a global fit
involving about 20
observables measured at the B-factories and by the CDF and LHCb collaborations.
A single evaluation of the likelihood requires approximately 1 s.
In addition to the 3 -- 12 parameters of interest, there are on the order of 25 nuisance
parameters describing uncertainties from standard model parameters as well as
from unknown higher order theory corrections and non-perturbative QCD effects.
The resulting posterior distribution is multi-modal and shows significant
correlation between parameters as well as pronounced degeneracies, hence
the standard MCMC methods fail to produce accurate results.
Parallelization is the only solution to obtain a sufficient number of samples in reasonable time.
We present an enhancement of existing MCMC algorithms, including the ability for massive parallelization
on a computing cluster and, more importantly,
a general scheme to induce rapid convergence even in the face complicated posterior distributions.
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Author
Frederik Beaujean
(Max Planck Institute for Physics)
Co-authors
Allen Caldwell
(Max Planck Institute for Physics)
Daniel Kollar
(Max Planck Institute for Physics)
Julia Grebenyuk
(DESY)
Kevin Alexander Kroeninger
(Georg-August-Universitaet Goettingen (DE))
Shabnaz Pashapouralamdari
(Georg-August-Universitaet Goettingen (DE))
