二项分布超过期望概率的紧下界
Tight Lower Bound on the Probability of a Binomial Exceeding its Expectation
Courant Institute of Mathematical Sciences（库朗数学科学研究所）；Google Research（谷歌研究院）
摘要：
证明了一个二项随机变量超过其期望值的概率的紧下界。该不等式在学习理论中的相对偏差界分析和无界损失函数的推广界分析等方面都起着重要的作用。
英文摘要：
We give the proof of a tight lower bound on the probability that a binomial random variable exceeds its expected value. The inequality plays an important role in a variety of contexts, including the analysis of relative deviation bounds in learning theory and generalization bounds for unbounded loss functions.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
