Good Randomized Sequential Probability Forecasting is Always Possible

DOI10.1111/j.1467-9868.2005.00525.xzbMath1112.62311OpenAlexW1999976395WikidataQ62046744 ScholiaQ62046744MaRDI QIDQ5490618

Glenn R. Shafer, Vladimir Vovk

Publication date: 4 October 2006

Published in: Journal of the Royal Statistical Society Series B: Statistical Methodology (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/j.1467-9868.2005.00525.x

zbMATH Keywords

calibrationprobability forecastinggame theoretic probability

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (14)

Randomization and entropy in machine learning and data processing ⋮ Can we estimate macroforecasters' mis-behavior? ⋮ Nonmanipulable Bayesian testing ⋮ A prequential test for exchangeable theories ⋮ Non-asymptotic calibration and resolution ⋮ Implications of contrarian and one-sided strategies for the fair-coin game ⋮ Game-theoretic versions of strong law of large numbers for unbounded variables ⋮ Probabilistic Forecasts, Calibration and Sharpness ⋮ A betting interpretation for probabilities and Dempster-Shafer degrees of belief ⋮ Mostly calibrated ⋮ Merging and testing opinions ⋮ On calibration error of randomized forecasting algorithms ⋮ A nonmanipulable test ⋮ Good Randomized Sequential Probability Forecasting is Always Possible

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• Algorithmic Learning Theory
• Good Randomized Sequential Probability Forecasting is Always Possible
• Calibration with Many Checking Rules

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