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Complex computer simulations are commonly required for accurate data modelling in many scientific disciplines, making statistical inference challenging due to the intractability of the likelihood evaluation for the observed data.
“Theory of Statistical Estimation”
R.. Fisher · 1925
Earlier work this paper cites.
“On the Problem of the Most Efficient Tests of Statistical Hypotheses”
J. Neyman and E.. Pearson · 1933
Earlier work this paper cites.
“Marginal and conditional sufficiency”
David Sprott · 1975
Earlier work this paper cites.
“Memoir on the probability of the causes of events”
Pierre Laplace · 1986
Earlier work this paper cites.
“Extended maximum likelihood”
Roger Barlow · 1990
Earlier work this paper cites.
“Information and the accuracy attainable in the estimation of statistical parameters”
C. Rao · 1992
Earlier work this paper cites.
“TMVA—Toolkit for Multivariate Data Analysis, in proceedings of 11th International Workshop on Advanced Computing and Analysis Techniques in Physics Research”
A Hocker · 2007
Earlier work this paper cites.
“Computing likelihood functions for high-energy physics experiments when distributions are defined by simulators with nuisance parameters”
Radford Neal · 2008
Earlier work this paper cites.
“Statistical inference for noisy nonlinear ecological dynamic systems”
Simon. Wood · 2010
Earlier work this paper cites.
“On partial sufficiency: A review”
D Basu · 2011
Earlier work this paper cites.
“Asymptotic formulae for likelihood-based tests of new physics”
Glen Cowan, Kyle Cranmer, Eilam Gross and Ofer Vitells · 2011
Cited alongside, same era.
“Observation of a new boson at a mass of 125 GeV with the CMS experiment at the LHC”
Serguei Chatrchyan et al · 2012
Cited alongside, same era.
“Observation of a new particle in the search for the Standard Model Higgs boson with the ATLAS detector at the LHC”
Georges Aad et al · 2012
Cited alongside, same era.
“Searching for exotic particles in high-energy physics with deep learning”
Pierre Baldi, Peter Sadowski and Daniel Whiteson · 2014
Cited alongside, same era.
“Approximating likelihood ratios with calibrated discriminative classifiers”
Kyle Cranmer, Juan Pavez and Gilles Louppe · 2015
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“Parameterized neural networks for high-energy physics”
Pierre Baldi et al · 2016
Later among the works it cites.
“Learning to Pivot with Adversarial Networks”
Gilles Louppe, Michael Kagan and Kyle Cranmer · 2017
Later among the works it cites.
Joshua Dillon et al · 2017
Later among the works it cites.
“Adversarial learning to eliminate systematic errors: a case study in High Energy Physics”
Victor Estrade, Cécile Germain, Isabelle Guyon and David Rousseau · 2017
Later among the works it cites.
“Mining gold from implicit models to improve likelihood-free inference”, 2018
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“TensorFlow: Large-Scale Machine Learning on Heterogeneous Systems” Software available from tensorflow.org, 2015
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Cited alongside, same era.
“Mathematical methods of statistics (PMS-9)”
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