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arXiv math/0405322math.PR
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随机对策树评价的概率分析

Probabilistic Analysis for Randomized Game Tree Evaluation

Tämur Ali Khan, Ralph Neininger

摘要:

对Snir的随机对策树评价算法进行了概率分析。我们首先表明,存在一个输入,这样的运行时间,测量的外部节点的数量读取的算法,在该输入是最大的随机顺序之间的所有可能的输入。对于这种最坏的情况下输入,我们确定的外部节点的数量的算法读取的确切期望,给出的方差,包括领先的常数的渐近顺序,提供了一个适当的规范化以及尾部边界估计大偏差的限制法律。我们的尾界提高了早期的约束由于卡普和张,其中subgaussian尾巴的基础上,使用多类型的分支过程和Azuma的不等式的方法。我们的方法依赖于一个直接的,归纳估计的矩母函数。

英文摘要:

We give a probabilistic analysis for the randomized game tree evaluation algorithm of Snir. We first show that there exists an input such that the running time, measured as the number of external nodes read by the algorithm, on that input is maximal in stochastic order among all possible inputs. For this worst case input we identify the exact expectation of the number of external nodes read by the algorithm, give the asymptotic order of the variance including the leading constant, provide a limit law for an appropriate normalization as well as a tail bound estimating large deviations. Our tail bound improves upon the exponent of an earlier bound due to Karp and Zhang, where subgaussian tails were shown based on an approach using multitype branching processes and Azuma's inequality. Our approach rests on a direct, inductive estimate of the moment generating function.

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。

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