不完善监测下的预测策略
Strategies for prediction under imperfect monitoring
摘要：
我们提出了简单的随机策略，序贯预测不完善的监测下，也就是说，当预测者没有访问过去的结果，而是一个反馈信号。所提出的策略是一致的，在这个意义上，他们实现，渐近，最好的平均奖励。Rustichini（1999）首先证明了这种一致性预测因子的存在。这里介绍的预测者提供了一致性的第一个建设性证据。此外，所提出的算法是计算效率。我们还建立了收敛速度的上界。在确定性反馈的情况下，这些速率是最佳的对数项。
英文摘要：
We propose simple randomized strategies for sequential prediction under imperfect monitoring, that is, when the forecaster does not have access to the past outcomes but rather to a feedback signal. The proposed strategies are consistent in the sense that they achieve, asymptotically, the best possible average reward. It was Rustichini (1999) who first proved the existence of such consistent predictors. The forecasters presented here offer the first constructive proof of consistency. Moreover, the proposed algorithms are computationally efficient. We also establish upper bounds for the rates of convergence. In the case of deterministic feedback, these rates are optimal up to logarithmic terms.
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