INFERNO: inference-aware neural optimisation

DOI10.1016/j.cpc.2019.06.007OpenAlexW2807858310WikidataQ127665612 ScholiaQ127665612MaRDI QIDQ2696520

Yanyan Li

Publication date: 14 April 2023

Published in: Computer Physics Communications (Search for Journal in Brave)

Full work available at URL: https://arxiv.org/abs/1806.04743

zbMATH Keywords

neural networksnuisance parametershigh energy physicslikelihood-free inference

Mathematics Subject Classification ID

Statistics (62-XX) Computer science (68-XX)

Related Items (2)

The frontier of simulation-based inference ⋮ Parametrized classifiers for optimal EFT sensitivity

Uses Software

• TensorFlow
• Edward
• GitHub

Cites Work

• Unnamed Item
• Weakly supervised classification in high energy physics
• Breakthroughs in statistics. Volume II: Methodology and distribution
• Selected Works of Debabrata Basu
• Marginal and conditional sufficiency
• Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network
• Mining gold from implicit models to improve likelihood-free inference

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