A New Vector Partition of the Probability Score

By A. H. Murphy. 1973

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Citations in the corpus

Verified Uncertainty Calibration (2019, methodology): “The importance of this notion of calibration has been emphasized in personalized medicine [1], meteorological forecasting [2, 3, 4, 5, 6] and natural language processing applications [7, 8].”
Calibrating Deep Convolutional Gaussian Processes (2018, background): “Another metric that measures the accuracy in predicting class probabilities is the brier score which takes into account the factors of calibration, resolution and uncertainty (Murphy, 1973).”
Accurate Uncertainties for Deep Learning Using Calibrated Regression (2018, background): “The study of calibration originates in the statistics literature (Murphy, 1973; Dawid, 1984), mainly in the context of evaluating probabilistic forecasts using proper loss functions (Gneiting and Raftery, 2007).”
Generalization of the Ignorance Score: Continuous Ranked Version and Its Decomposition (2012, background): “To gain more insight into aspects of forecast quality, it can be useful to decompose the BS (Murphy 1973).”
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.”
Toward an Improved Multimodel ENSO Prediction (2015, background): “The six models include 1 Probabilistic reliability (Murphy 1973;Wilks 2006) refers to the condition that for a sufficiently large set of all forecasts of a given probability for an event (such as a 40% likelihood for an El Niño), the corresponding relative frequency of later observed occurrence of…”
Ensemble forecasting (2008, background): “These two aspects are referred to as reliability and resolution [19].”
Of quantiles and expectiles: consistent scoring functions, Choquet representations and forecast rankings (2015, background): “Different aspects of a forecast may be of practical interest, even to the same user, e.g. reliability and resolution in the decomposition of the Brier score for probability forecasts (Murphy, 1973) or mean-square error and correlation for continuous forecasts (Murphy, 1988).”
Comparison of Ensemble-MOS Methods Using GFS Reforecasts (2007, methodology): “Here the OLS forecasts are slightly more reliable [using the Murphy (1973), decomposition of the Brier score, as shown in the inset] than the NGR forecasts.”
Seasonal predictions of precipitation over Africa using coupled ocean-atmosphere general circulation models: skill of the ENSEMBLES project multimodel ensemble forecasts (2011, methodology): “As suggested in Murphy (1973) and Wilks (2006), the Brier Score can be written as the sum of three terms by sorting the forecastobservation pairs according to the J possible forecast values {yj, j ∈ 1, .., J} for a single event.”
The Comparison and Evaluation of Forecasters. (1983, background): “In meteorology, the concept of calibration is called validity (Miller, 1962) or reliability (Murphy, 1973), and a well-calibrated forecaster is called perfectly reliable.”