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Welcome to hepstats’s documentation!

hepstats is a python library providing statistics tools and utilities for particles physics. It can be installed It can be installed with pip from PyPI.

Indices and tables

bibliography

BB_

Dynamic Programming in Python: Bayesian Blocks, howpublished = http://jakevdp.github.io/blog/2012/09/12/dynamic-programming-in-python/. Accessed: 2020-11-03.

PLD05

Muriel Pivk and Francois R. Le Diberder. SPlot: A Statistical tool to unfold data distributions. Nucl. Instrum. Meth., A555:356–369, 2005. arXiv:physics/0402083, doi:10.1016/j.nima.2005.08.106.

PBS17

Brian Pollack, Saptaparna Bhattacharya, and Michael Schmitt. Bayesian Block Histogramming for High Energy Physics. 2017. arXiv:1708.00810.

SNJC13

Jeffrey D. Scargle, Jay P. Norris, Brad Jackson, and James Chiang. Studies in astronomical time series analysis. vi. bayesian block representations. The Astrophysical Journal, 764(2):167, Feb 2013. URL: http://dx.doi.org/10.1088/0004-637X/764/2/167, doi:10.1088/0004-637x/764/2/167.

VCIG12

Jacob VanderPlas, Andrew J. Connolly, Zeljko Ivezic, and Alex Gray. Introduction to astroml: machine learning for astrophysics. 2012 Conference on Intelligent Data Understanding, Oct 2012. URL: http://dx.doi.org/10.1109/CIDU.2012.6382200, doi:10.1109/cidu.2012.6382200.