论文标题
PYHF：带有张量和自动差异的历史悠久的纯净python实施
pyhf: pure-Python implementation of HistFactory with tensors and automatic differentiation
论文作者
论文摘要
历史记录P.D.F.模板独立于其在根中的实现，并且能够在根，屋顶，栖息地框架之外运行统计分析很有用。 PYHF是基于多键直方图分析的该统计模型的纯粹实现，其间隔估计基于“基于可能基于可能的新物理学的可能性测试”的“渐近公式”的渐近公式。 PYHF支持现代计算图库，例如TensorFlow，Pytorch和Jax，以利用自动差异和GPU加速度等功能。此外，针对Histfactory模型的PYHF的JSON序列化规范已用于将已发布的ATLAS协作分析分析的23个完整概率模型发布到HEPDATA。
The HistFactory p.d.f. template is per-se independent of its implementation in ROOT and it is useful to be able to run statistical analysis outside of the ROOT, RooFit, RooStats framework. pyhf is a pure-Python implementation of that statistical model for multi-bin histogram-based analysis and its interval estimation is based on the asymptotic formulas of "Asymptotic formulae for likelihood-based tests of new physics". pyhf supports modern computational graph libraries such as TensorFlow, PyTorch, and JAX in order to make use of features such as auto-differentiation and GPU acceleration. In addition, pyhf's JSON serialization specification for HistFactory models has been used to publish 23 full probability models from published ATLAS collaboration analyses to HEPData.
