We are always interested in talking about pyhf
. See the abstract and a list of previously given presentations and feel free to invite us to your next conference/workshop/meeting!
The HistFactory p.d.f. template [CERN-OPEN-2012-016] 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"
arXiv:1007.1727
. 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.The HistFactory p.d.f. template \href{https://cds.cern.ch/record/1456844}{[CERN-OPEN-2012-016]} 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" \href{https://arxiv.org/abs/1007.1727}{[arXiv:1007.1727]}. 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.
This list will be updated with talks given on pyhf
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bib/talks.bib
This list will be updated with tutorials and schools given on pyhf
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bib/tutorials.bib
This list will be updated with posters presented on pyhf
:
bib/posters.bib
This list will be updated with media publications featuring pyhf
:
bib/media.bib