避免分箱拟合中的偏差
Avoiding biases in binned fits
摘要：
在分析大型数据集时，分箱极大似然拟合是一个有吸引力的选择，但在计算箱中连续PDF的似然性时需要小心。多年来，广泛使用的统计建模包RooFit评估箱中心的概率，导致强烈弯曲的概率密度函数的显着偏差。我们展示了与现实世界的例子的偏见，并引入一个PDF类RooFit，消除这些偏见。这种新的类的物理和计算性能进行了讨论。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
