Avoiding biases in binned fits
Data Analysis, Statistics and Probability
2021-08-06 v2
Abstract
Binned maximum likelihood fits are an attractive option when analysing large datasets, but require care when computing likelihoods of continuous PDFs in bins. For many years the widely used statistical modelling package RooFit evaluated probabilities at the bin centre, leading to significant biases for strongly curved probability density functions. We demonstrate the biases with real-world examples, and introduce a PDF class to RooFit that removes these biases. The physics and computation performance of this new class are discussed.
Cite
@article{arxiv.2104.13879,
title = {Avoiding biases in binned fits},
author = {V. V. Gligorov and S. Hageboeck and T. Nanut and A. Sciandra and D. Y. Tou},
journal= {arXiv preprint arXiv:2104.13879},
year = {2021}
}
Comments
14 pages, 12 figures, submitted to JINST v2: Minor revisions as suggested during review