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Data analysis in science, e.g., high-energy particle physics, is often subject to an intractable likelihood if the observables and observations span a high-dimensional input space.
Theory of statistical estimation
Fisher, R.A.: · 1925
Earlier work this paper cites.
Information and the accuracy attainable in the estimation of statistical parameters
Rao, C.R.: · 1992
Earlier work this paper cites.
Mathematical methods of statistics. Volume 9
Cramér, H.: · 1999
Earlier work this paper cites.
The RooFit toolkit for data modeling (2003)
Verkerke, W., Kirkby, D.: · 2003
Earlier work this paper cites.
Statistical methods in experimental physics
James, F.: · 2006
Earlier work this paper cites.
ROOT - A C++ framework for petabyte data storage, statistical analysis and visualization
Antcheva, I., Ballintijn, M., Bellenot, B., et al.: · 2009
Earlier work this paper cites.
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Glorot, X., Bengio, Y.: · 2010
Earlier work this paper cites.
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Moneta, L., Belasco, K., Cranmer, K.S., Kreiss, S., Lazzaro, A., Piparo, D., Schott, G., Verkerke, W., Wolf, M.: · 2010
Earlier work this paper cites.
Asymptotic formulae for likelihood-based tests of new physics
Cowan, G., Cranmer, K., Gross, E., Vitells, O.: · 2011
Earlier work this paper cites.
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Earlier work this paper cites.
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Earlier work this paper cites.
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Cited alongside, same era.
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Cited alongside, same era.
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Cited alongside, same era.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Later among the works it cites.
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Cited alongside, same era.
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Cited alongside, same era.
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