Consistent, multidimensional differential histogramming and summary statistics with YODA 2
arxiv(2023)
摘要
Histogramming is often taken for granted, but the power and compactness of
partially aggregated, multidimensional summary statistics, and their
fundamental connection to differential and integral calculus make them
formidable statistical objects, especially when very large data volumes are
involved. But expressing these concepts robustly and efficiently in
high-dimensional parameter spaces and for large data samples is a highly
non-trivial challenge – doubly so if the resulting library is to remain usable
by scientists as opposed to software engineers. In this paper we summarise the
core principles required for consistent generalised histogramming, and use them
to motivate the design principles and implementation mechanics of the
re-engineered YODA histogramming library, a key component of physics data-model
comparison and statistical interpretation in collider physics.
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