Speaker
Dr
Brian Pollack
(Northwestern University)
Description
The Bayesian Block algorithm, originally developed for applications in astronomy, can be used to improve the binning of histograms in high energy physics. Along with visual improvements, the histogram produced from this algorithm is a non-parametric density estimate, providing a description of background distributions that do not suffer from the arbitrariness of ad-hoc analytical functions. The statistical power of a hypothesis test based on a Bayesian Blocks binned template is nearly as good as one obtained by fitting analytical functions. This presentation will showcase the visual and statistical benefits of the Bayesian Blocks algorithm on a handful of examples based on common HEP analyses.
Primary author
Dr
Brian Pollack
(Northwestern University)
Co-authors
Michael Schmitt
(Northwestern University)
Ms
Saptaparna Bhattacharya
(Brown University)