Reliability, sufficiency, and the decomposition of proper scores

By Jochen Bröcker. 2009

40 citations recorded by the source corpus.

Subfields: Atmospheric Science, Global and Planetary Change, Management Science and Operations Research

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Citations in the corpus

Dark Experience for General Continual Learning: a Strong, Simple Baseline (2020, background): “weather forecasting [5] or econometric analysis [13]); therefore, calibration represents an appealing property for any CL system aiming for employment outside of a laboratory environment.”
Calibrated Structured Prediction (2015, background): “Central to this paper is the idea of probability calibration [6, 7, 8, 9], which is prominent in the meteorology [10] and econometrics [9] literature.”
Evaluating Scalable Bayesian Deep Learning Methods for Robust Computer Vision (2019, methodology): “As an absolute measure of uncertainty estimation quality, we therefore also evaluate the methods in terms of calibration [5, 46].”
Estimating reliability and resolution of probability forecasts through decomposition of the empirical score (2012, methodology): “The connection between scoring rules and the attributes reliability and resolution has been clarified in a series of papers (Murphy, 1973, 1996; Hersbach, 2000; Bröcker, 2009).”
The Importance of Calibration for Estimating Proportions from Annotations (2018, background): “It has previously been shown (DeGroot and Fienberg, 1983; Bröcker, 2009) that any proper scoring rule (e.”
Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings (2015, background): “In the special case of probability forecasts of a binary event, related notions of sufficiency and dominance have been studied by DeGroot and Fienberg (1983), Vardeman and Meeden (1983), Schervish (1989), Feuerverger and Rahman (1992), Krämer (2005) and Bröcker (2009).”