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Asymptotic calibration
Open accessAbstract
Can we forecast the probability of an arbitrary sequence of events happening so that the stated probability of an event happening is close to its empirical probability? We can view this prediction problem as a game played against Nature, where at the beginning of the game Nature picks a data sequence and the forecaster picks a forecasting algorithm. If the forecaster is not allowed to randomise, then Nature wins; there will always be data for which the forecaster does poorly. This paper shows that, if the forecaster can randomise, the forecaster wins in the sense that the forecasted probabilities and the empirical probabilities can be made arbitrarily close to each other.
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Citations
Cited by
Probabilistic Forecasts, Calibration and Sharpness
Gneiting, T., Balabdaoui, F., & Raftery, A. E. (2007) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.1467-9868.2007.00587.x · Cited by 1,783
Lepri, B., Oliver, N., Letouzé, E., Pentland, A., & Vinck, P. (2018) · Philosophy & Technology · doi:10.1007/s13347-017-0279-x · Cited by 709
Game theory, on-line prediction and boosting
Freund, Y., & Schapire, R. E. (1996) · Proceedings of the ninth annual conference on Computational learning theory - COLT '96 · doi:10.1145/238061.238163 · Cited by 383
Algorithmic monoculture and social welfare
(2023) · The Societal Impacts of Algorithmic Decision-Making · doi:10.1145/3603195.3603202
Metadata
- Authors
- Foster, D.
- Venue
- Biometrika
- Publisher
- Oxford University Press (OUP)
- Published
- 1998-06-01
- Volume
- 85
- Issue
- 2
- Pages
- 379-390
- ISSN
- 0006-3444 (ISSN-L)
- 1464-3510
- OpenAlex
- W4255572858
- Cited by
- 204
- References
- 0
- FWCI
- 3.07
- Type
- journal article
- Language
- en
- Open access
- green
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- Foster, D. (1998). Asymptotic calibration. Biometrika. https://doi.org/10.1093/biomet/85.2.379
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
