Computing Likelihood Functions for High-Energy Physics Experiments when Distributions are Defined by Simulators with Nuisance Parameters

By Radford M. Neal. 2008

14 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Information Systems and Management, Statistics and Probability, AI for Physical Sciences

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Citations in the corpus

Learning in Implicit Generative Models (2016, methodology): “[19] ca ll this approach unsupervised-as-supervised learning, Qin [31] explore this for analysis of case-contro l in observational studies, both Neal [28] and Cranmer et al.”
Approximating Likelihood Ratios with Calibrated Discriminative Classifiers (2015, methodology): “This point was made by Neal (2007) and is well appreciated by the particle physics community that typically takes a conservative attitude towards the use of machine learning classiﬁers precisely due to concerns about the calibration of p -values in the face of nuisance parameters associated to the…”
Under Review as a Conference Paper at Iclr 2017 Learning in Implicit Generative Models (2017, background): “…(2013) call this approach unsupervised-assupervised learning, Qin (1998) explore this for analysis of case-control in observational studies, both Neal (2008) and Cranmer et al. (2015) explore this approach for high-energy physics applications, Gutmann and Hyvärinen (2012) exploit it for…”
INFERNO: Inference-Aware Neural Optimisation (2018, background): “The ﬁrst known effort to include the effect of nuisance parameters in classiﬁcation and explain the relation between classiﬁcation and the likelihood ratio was by Neal [18].”
Learning to Pivot with Adversarial Networks (2016, background): “As argued in [25], it is not obvious how such classifiers can be used on real data since the correct value z of the nuisance often remains unknown.”