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Optimal statistical inference in the presence of systematic uncertainties using neural network optimization based on binned Poisson likelihoods with nuisance parameters
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. Typically the problem is solved by reducing the dimensionality using feature engineering and histograms, whereby the la...
| Autores principales: | , , , |
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| Lenguaje: | eng |
| Publicado: |
2020
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| Materias: | |
| Acceso en línea: | https://dx.doi.org/10.1007/s41781-020-00049-5 http://cds.cern.ch/record/2751415 |