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Scoring rules and the evaluation of probabilities

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Summary

In Bayesian inference and decision analysis, inferences and predictions are inherently probabilistic in nature. Scoring rules, which involve the computation of a score based on probability forecasts and what actually occurs, can be used to evaluate probabilities and to provide appropriate incentives for “good” probabilities. This paper review scoring rules and some related measures for evaluating probabilities, including decompositions of scoring rules and attributes of “goodness” of probabilites, comparability of scores, and the design of scoring rules for specific inferential and decision-making problems

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Authors and Affiliations

  1. Fuqua School of Business and Institute of Statistics and Decision Sciences, Duke University, 27708-0120, Durham, NC, USA

    R. L. Winkler

  2. Generalitat Valenciana, Spain

    Javier Muñoz (Presidència)

  3. Instituto Nacional de Estadística, Spain

    José L. Cervera

  4. Universitat de València, Spain

    José M. Bernardo

  5. University of Utah, USA

    Gail Blattenberger

  6. Carnegie Mellon University, USA

    Joseph B. Kadane

  7. Minehead, UK

    Dennis V. Lindley

  8. Prediction and Evaluation Systems, USA

    Allan H. Murphy

  9. University of California at Berkeley, USA

    Robert M Oliver

  10. Universidad Politécnica de Madrid, Madrid, Espana

    David Ríos-Insua

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  1. R. L. Winkler
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  2. Javier Muñoz
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  3. José L. Cervera
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  4. José M. Bernardo
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  8. Allan H. Murphy
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Read before the Spanish Statistical Society at a meeting organized by the Universitat de València on Tuesday, April 23, 1996

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Winkler, R.L., Muñoz, J., Cervera, J.L. et al. Scoring rules and the evaluation of probabilities. Test 5, 1–60 (1996). https://doi.org/10.1007/BF02562681

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