论文标题
使用未经证实的知识的集中不平等
Concentration inequality using unconfirmed knowledge
论文作者
论文摘要
我们基于随机变量在特定区域内采用值的前提来给出浓度不平等。浓度不等式保证，对于任何相关的随机变量，有条件期望和观察值的差异之间的差异在已知的情况下评估了预期值时，概率很小，概率很高。我们的不平等超过其他众所周知的不平等，例如Azuma-Hoeffding不平等，特别是在随机变量高度偏见时的收敛速度方面。我们预测一些参数并在不平等中采用预测值的关键思想提供了我们不平等的高性能。
We give a concentration inequality based on the premise that random variables take values within a particular region. The concentration inequality guarantees that, for any sequence of correlated random variables, the difference between the sum of conditional expectations and that of the observed values takes a small value with high probability when the expected values are evaluated under the condition that the past values are known. Our inequality outperforms other well-known inequalities, e.g. the Azuma-Hoeffding inequality, especially in terms of the convergence speed when the random variables are highly biased. This high performance of our inequality is provided by the key idea in which we predict some parameters and adopt the predicted values in the inequality.
