Well-calibrated, coherent forecasting systems
نویسندگان
چکیده
منابع مشابه
Calibrated Forecasting and Merging
Consider a finite-state stochastic process governed by an unknown objective probability distribution. Observing the system, a forecaster assigns subjective probabilities to future states. The resulting subjective forecast merges to the objective distribution if, with time, the forecasted probabilities converge to the Ž . correct but unknown probabilities. The forecast is calibrated if observed ...
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Consider a general finite-state stochastic process governed by an unknown objective probability distribution. Observing the system, a forecaster assigns subjective probabilities to future states. The resulting subjective forecast merges to the objective distribution if, with time, the forecasted probabilities converge to the correct (but unknown) probabilities. The forecast is calibrated if obs...
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We recall two previously-proposed notions of asymptotic calibration for a forecaster making a sequence of probability predictions. We note that the existence of efficient algorithms for calibrated forecasting holds only in the case of binary outcomes. We pose the question: do there exist such efficient algorithms for the general (non-binary) case? Review of Calibrated Forecasting Glenn Brier, w...
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Learning probabilistic predictive models that are well calibrated is critical for many prediction and decision-making tasks in artificial intelligence. In this paper we present a new non-parametric calibration method called Bayesian Binning into Quantiles (BBQ) which addresses key limitations of existing calibration methods. The method post processes the output of a binary classification algori...
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ژورنال
عنوان ژورنال: Теория вероятностей и ее применения
سال: 1997
ISSN: 0040-361X
DOI: 10.4213/tvp1717