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arXiv 1111.0568math.PR
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Benford定律与连续相依随机变量

Benford's Law and Continuous Dependent Random Variables

Thealexa Becker, Alec Greaves-Tunnell, Steven J. Miller, Ryan Ronan, Frederick W. Strauch

摘要:

许多系统都存在数字偏置。例如,斐波那契数或$2^n$的以10为基数的第一位数在10%或11%的情况下等于1,而不是像人们所期望的那样,如果所有数字的可能性相同,而是大约30%的时间。这一现象被称为本福德定律,有许多应用,从为国税局检测税务欺诈到分析计算机科学中的舍入误差。核心问题是确定哪些数据集遵循本福德定律。受粒子衰变等自然过程的启发,我们的工作研究了守恒量分解的模型。我们证明,在许多情况下,所产生的碎片的长度分布收敛到本福德行为的部门的数量增长。主要的困难是,由此产生的随机变量是相关的,我们通过仔细分析的依赖关系和傅里叶分析的工具来处理,以获得量化的收敛速度。

英文摘要:

Many systems exhibit a digit bias. For example, the first digit base 10 of the Fibonacci numbers, or of $2^n$, equals 1 not 10% or 11% of the time, as one would expect if all digits were equally likely, but about 30% of the time. This phenomenon, known as Benford's Law, has many applications, ranging from detecting tax fraud for the IRS to analyzing round-off errors in computer science. The central question is determining which data sets follow Benford's law. Inspired by natural processes such as particle decay, our work examines models for the decomposition of conserved quantities. We prove that in many instances the distribution of lengths of the resulting pieces converges to Benford behavior as the number of divisions grow. The main difficulty is that the resulting random variables are dependent, which we handle by a careful analysis of the dependencies and tools from Fourier analysis to obtain quantified convergence rates.

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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