MadMiner: Machine Learning-Based Inference for Particle Physics
- 18 January 2020
- journal article
- research article
- Published by Springer Nature in Computing and Software for Big Science
- Vol. 4 (1) , 1-25
Abstract
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Funding Information
- National Science Foundation (ACI-1450310, OAC-1836650)
- National Science Foundation (OAC-1841471, PHY-1505463)
This publication has 41 references indexed in Scilit:
- Unravellingvia the Matrix Element MethodPhysical Review Letters, 2013
- Asymptotic formulae for likelihood-based tests of new physicsThe European Physical Journal C, 2011
- A brief introduction to PYTHIA 8.1Computer Physics Communications, 2008
- Kernel estimation in high-energy physicsComputer Physics Communications, 2001
- The HepMC C++ Monte Carlo event record for High Energy PhysicsComputer Physics Communications, 2001
- The optimal method for the measurement of tau polarizationPhysics Letters B, 1993
- Bayesianly Justifiable and Relevant Frequency Calculations for the Applied StatisticianThe Annals of Statistics, 1984
- Differential Geometry of Curved Exponential Families-Curvatures and Information LossThe Annals of Statistics, 1982
- Tests of statistical hypotheses concerning several parameters when the number of observations is largeTransactions of the American Mathematical Society, 1943
- The Large-Sample Distribution of the Likelihood Ratio for Testing Composite HypothesesThe Annals of Mathematical Statistics, 1938