449 citations · 2.1k across the 35 of their papers we have counts for
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Assessing Monotone Dependence: Area Under the Curve Meets Rank Correlation
Eva-Maria Walz, Andreas Eberl, Tilmann Gneiting
The assessment of monotone dependence between random variables is a classical problem in statistics and a gamut of application domains. Consequently, researchers have sought measur…
Decompositions of the mean continuous ranked probability score
Sebastian Arnold, Eva-Maria Walz, Johanna Ziegel +1
The continuous ranked probability score (crps) is the most commonly used scoring rule in the evaluation of probabilistic forecasts for real-valued outcomes. To assess and rank fore…
Evaluating Probabilistic Classifiers: The Triptych
Timo Dimitriadis, Tilmann Gneiting, Alexander I. Jordan +1
Probability forecasts for binary outcomes, often referred to as probabilistic classifiers or confidence scores, are ubiquitous in science and society, and methods for evaluating an…
Easy Uncertainty Quantification (EasyUQ): Generating Predictive Distributions from Single-valued Model Output
Eva-Maria Walz, Alexander Henzi, Johanna Ziegel +1
How can we quantify uncertainty if our favorite computational tool - be it a numerical, a statistical, or a machine learning approach, or just any computer model - provides single-…
Regression Diagnostics meets Forecast Evaluation: Conditional Calibration, Reliability Diagrams, and Coefficient of Determination
Tilmann Gneiting, Johannes Resin
Model diagnostics and forecast evaluation are two sides of the same coin. A common principle is that fitted or predicted distributions ought to be calibrated or reliable, ideally i…
Evaluating probabilistic classifiers: Reliability diagrams and score decompositions revisited
Timo Dimitriadis, Tilmann Gneiting, Alexander I. Jordan
A probability forecast or probabilistic classifier is reliable or calibrated if the predicted probabilities are matched by ex post observed frequencies, as examined visually in rel…