2010/09/06 by L. Moneta, Lorenzo Moneta, Moneta, Lorenzo +21 · 43 citations
Computer Science · Physics and Astronomy · #Computational Physics and Python Applications #Data Analysis #FOS: Physical sciences #Particle Detector Development and Performance #Particle physics theoretical and experimental studies #Statistics and Probability (physics.data-an) #physics.data-an
paper · pdf · doi:10.48550/arxiv.1009.1003
11 pages, 3 figures, ACAT2010 Conference Proceedings
openalex publication_date 2010/09/06 · arxiv created 2011/02/01 · arxiv updated 2011/02/02 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
RooStats is a project to create advanced statistical tools required for the analysis of LHC data, with emphasis on discoveries, confidence intervals, and combined measurements. The idea is to provide the major statistical techniques as a set of C++ classes with coherent interfaces, so that can be used on arbitrary model and datasets in a common way. The classes are built on top of the RooFit package, which provides functionality for easily creating probability models, for analysis combinations and for digital publications of the results. We will present in detail the design and the implementation of the different statistical methods of RooStats. We will describe the various classes for interval estimation and for hypothesis test depending on different statistical techniques such as those based on the likelihood function, or on frequentists or bayesian statistics. These methods can be applied in complex problems, including cases with multiple parameters of interest and various nuisance parameters.