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<!DOCTYPE html>
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<h1>Welcome to hepstats’s documentation!<a class="headerlink" href="#welcome-to-hepstats-s-documentation" title="Permalink to this headline">¶</a></h1>
<p>hepstats is a python library providing statistics tools and utilities for particles physics. It can be installed
It can be installed with <code class="docutils literal notranslate"><span class="pre">pip</span></code> from <a class="reference external" href="https://pypi.org/project/hepstats">PyPI</a>.</p>
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<h1>Indices and tables<a class="headerlink" href="#indices-and-tables" title="Permalink to this headline">¶</a></h1>
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<h1>bibliography<a class="headerlink" href="#bibliography" title="Permalink to this headline">¶</a></h1>
<p id="bibtex-bibliography-index-0"><dl class="citation">
<dt class="bibtex label" id="bb-jakevdp"><span class="brackets">BB_</span></dt>
<dd><p>Dynamic Programming in Python: Bayesian Blocks, howpublished = http://jakevdp.github.io/blog/2012/09/12/dynamic-programming-in-python/. Accessed: 2020-11-03.</p>
</dd>
<dt class="bibtex label" id="pivk-2004ty"><span class="brackets">PLD05</span></dt>
<dd><p>Muriel Pivk and Francois R. Le Diberder. SPlot: A Statistical tool to unfold data distributions. <em>Nucl. Instrum. Meth.</em>, A555:356–369, 2005. <a class="reference external" href="https://arxiv.org/abs/physics/0402083">arXiv:physics/0402083</a>, <a class="reference external" href="https://doi.org/10.1016/j.nima.2005.08.106">doi:10.1016/j.nima.2005.08.106</a>.</p>
</dd>
<dt class="bibtex label" id="pollack-2017srh"><span class="brackets">PBS17</span></dt>
<dd><p>Brian Pollack, Saptaparna Bhattacharya, and Michael Schmitt. Bayesian Block Histogramming for High Energy Physics. 2017. <a class="reference external" href="https://arxiv.org/abs/1708.00810">arXiv:1708.00810</a>.</p>
</dd>
<dt class="bibtex label" id="scargle-2013"><span class="brackets">SNJC13</span></dt>
<dd><p>Jeffrey D. Scargle, Jay P. Norris, Brad Jackson, and James Chiang. Studies in astronomical time series analysis. vi. bayesian block representations. <em>The Astrophysical Journal</em>, 764(2):167, Feb 2013. URL: <a class="reference external" href="http://dx.doi.org/10.1088/0004-637X/764/2/167">http://dx.doi.org/10.1088/0004-637X/764/2/167</a>, <a class="reference external" href="https://doi.org/10.1088/0004-637x/764/2/167">doi:10.1088/0004-637x/764/2/167</a>.</p>
</dd>
<dt class="bibtex label" id="vanderplas-2012"><span class="brackets">VCIG12</span></dt>
<dd><p>Jacob VanderPlas, Andrew J. Connolly, Zeljko Ivezic, and Alex Gray. Introduction to astroml: machine learning for astrophysics. <em>2012 Conference on Intelligent Data Understanding</em>, Oct 2012. URL: <a class="reference external" href="http://dx.doi.org/10.1109/CIDU.2012.6382200">http://dx.doi.org/10.1109/CIDU.2012.6382200</a>, <a class="reference external" href="https://doi.org/10.1109/cidu.2012.6382200">doi:10.1109/cidu.2012.6382200</a>.</p>
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