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On sums of independent random variables with unbounded variance, and estimating the average degree in a graph

机译:关于具有无界方差的独立随机变量的总和,并估计图中的平均程度

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We prove the following inequality: for every positive integer n and every collection X1,..., Xn of nonnegative independent random variables that each has expectation 1, the probability that their sum remains below n+1 is at least α 0. Our proof produces a value of α = 1/13 ≅ 0.077, but we conjecture that the inequality also holds with α = 1/e ≅ 0.368.As an example for the use of the new inequality, we consider the problem of estimating the average degree of a graph by querying the degrees of some of its vertices. We show the following threshold behavior: approximation factors above 2 require far less queries than approximation factors below 2. The new inequality is used in order to get tight (up to multiplicative constant factors) relations between the number of queries and the quality of the approximation. We show how the degree approximation algorithm can be used in order to quickly find those edges in a network that belong to many shortest paths.
机译:我们证明以下不等式:对于每个正负整数n和每个负期望值为1的非负独立随机变量X 1 ,...,X n 的集合,概率为它们的总和保持在n + 1以下至少为α>0。我们的证明产生了一个α= 1/13≅0.077的值,但是我们推测不等式在α= 1 / e≅0.368的情况下也成立。在使用新的不等式时,我们考虑了通过查询其某些顶点的度来估计图的平均度的问题。我们显示以下阈值行为:大于2的逼近因子比小于2的逼近因子所需要的查询少得多。使用新的不等式来获得查询数量和逼近质量之间的紧密关系(最多为乘性常数因子) 。我们展示了如何使用度近似算法来快速找到网络中属于许多最短路径的那些边缘。

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