VERIFICATION OF FORECASTS EXPRESSED IN TERMS OF PROBABILITY
By G. Brier. 1950
Subfields: Evaluation & Benchmarks
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In Evaluation & Benchmarks
- Balaji Lakshminarayanan
- Tilmann Gneiting
- Fotios Petropoulos
- Dustin Tran
- Percy Liang
- Nikolaos Kourentzes
- Andrew Gelman
- Jasper Snoek
Citations in the corpus
- MixMatch: A Holistic Approach to Semi-Supervised Learning (2019, background): “(4) (the multiclass Brier score [5]) because, unlike the cross-entropy, it is bounded and less sensitive to incorrect predictions.”
- Calibrate Before Use: Improving Few-Shot Performance of Language Models (2021, background): “This goal is distinct from statistical calibration (Brier, 1950; Guo et al., 2017), i.e., aligning a model’s confidence estimate with its true accuracy.”
- Simple and Scalable Predictive Uncertainty Estimation using Deep Ensembles (2016, methodology): “The quality of calibration can be measured by proper scoring rules (Gneiting and Raftery, 2007) such as log predictive probabilities and the Brier score (Brier, 1950).”
- Verified Uncertainty Calibration (2019, methodology): “The objective is to minimize the mean-squared error, also known as the Brier score [22], subject to a calibration error budget [5].”
- Measuring Calibration in Deep Learning (2019, methodology): “Unlike the Brier score (Brier, 1950) and Hosmer-Lemeshow (Hosmer & Lemesbow, 1980), we’d like the metric to be scalar-valued in order to easily benchmark methods, and to only measure calibration.”
- Can You Trust Your Model's Uncertainty? Evaluating Predictive Uncertainty Under Dataset Shift (2019, background): “Brier Score # (Brier, 1950) Proper scoring rule for measuring the accuracy of predicted probabilities.”
- Posterior calibration and exploratory analysis for natural language processing models (2015, background): “…data, such as the familiar cross entropy (negative average log-likelihood), or mean squared error, also known as the Brier score when y is binary (Brier, 1950), Both tend to attain better (lower) values when q is near 1 when y = 1 , and near 0 when y = 0 ; and they achieve a perfect value of 0…”
- ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring (2019, methodology): “A standard cross-entropy loss is used for labeled data, whereas the loss for unlabeled data is computed using a mean square error (i.e. the Brier score (Brier, 1950)) and is weighted with a hyperparameter λ U .”
- Deep Ensembles: A Loss Landscape Perspective (2019, methodology): “[2019], we measure accuracy as well as Brier score [Brier, 1950] (lower values indicate better uncertainty estimates).”
- Efficient Sequential Decision Making (2017, background): “The former is traditionally popular in meteorology [10] and the latter has connections with online Gaussian density estimation [50].”
- Stated Beliefs and Play in Normal Form Games (2008, unknown): “Brier, G. W. (1950): Verification of Forecasts Expressed in Terms of Probability, Monthly Weather Review, 78, 1-3. Camerer, Colin, Ho, Teck, and Chong, Juin-Kuan (2004), A Cognitive Hierarchy Model of Games, forthcoming, Quarterly Journal of Economics. Camerer, Colin, Ho, Teck, Chong, Juin-Kuan,”
- PROPER LOCAL SCORING RULES ON DISCRETE SAMPLE SPACES (2011, unknown): “Examples of proper scoring rules are the Brier score S(x, p) = ‖p‖(2)−2px [Brier (1950)], where ‖p‖ 2 = ∑ x p 2 x, with 1-homogeneous entropy functionH(α) =−‖α‖(2)/α+ and the spherical score S(x,p) =−px/‖p‖ with 1-homogeneous entropy function H(α) = −‖α‖ [Good (1971), Dawid (2007)].”
- A Hybrid Ensemble Kalman Filter-3D Variational Analysis Scheme (2000, methodology): “The quality of the forecasts were evaluated using Brier scores (Brier 1950; W ilks 1995), a measure of the mean-squared error of probabilistic forecasts.”
- Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration (2019, background): “Meteorologists were among the first to think about calibration, with [3] introducing an evaluation measure for probabilistic forecasts, which we now call Brier score; [21] proposing reliability diagrams, which allow us”
- Predicting mortgage default using convolutional neural networks (2018, methodology): “In addition to Accuray, Sensitivity, Specificity and AUC, the Brier Score (Brier, 1950) and the H-measure (Hand, 2009, 2010) are reported.”