Outreach#

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!

Abstract#

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” [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.

Presentations#

This list will be updated with talks given on pyhf:

Tutorials#

This list will be updated with tutorials and schools given on pyhf:

Posters#

This list will be updated with posters presented on pyhf:

In the Media#

This list will be updated with media publications featuring pyhf:

On this page
Show Source