The Well-Calibrated Bayesian
研究了预测者连续为事件分配概率时的校准问题，证明了一致贝叶斯预测者预期自身是良好校准的，并探讨了这一结论对一致性理论的破坏性影响。
Abstract Suppose that a forecaster sequentially assigns probabilities to events. He is well calibrated if, for example, of those events to which he assigns a probability 30 percent, the long-run proportion that actually occurs turns out to be 30 percent. We prove a theorem to the effect that a coherent Bayesian expects to be well calibrated, and consider its destructive implications for the theory of coherence.