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arXiv 2003.14025math.PRcs.ITmath.IT
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关于Martin-Löf随机数的中心极限定理

Central Limit Theorems for Martin-Löf Random Numbers

Anton Vuerinckx, Yves Moreau

摘要:

证明了与Martin-Löf随机序列的中心极限定理有关的两个定理。Martin-Löf随机性试图捕捉一个比特序列“真正随机”的含义。相比之下,CLT不对单个随机序列的行为做出断言,而仅对随机变量序列的分布行为做出断言。在语义上,我们通常将CLT解释为关于无限多个序列的集体行为的断言。然而,我们的直觉是,如果一个比特序列是“真正随机的”,那么它应该提供一个“随机性的来源”,CLT类型的结果应该成立。我们解决这个困难,通过使用一个采样方案,从一个单一的二进制序列产生一个无限的样本个数。我们表明,当我们应用这个计划的Martin-Löf随机序列,这些样本的经验矩和累积密度函数(CDF)趋于对应的对应物的正态分布。我们还证明了著名的几乎必然中心极限定理(ASCLT),它提供了一个替代的,虽然不太直观,回答这个问题。这两个结果也推广到Schnorr随机序列。

英文摘要:

We prove two theorems related to the Central Limit Theorem (CLT) for Martin-Löf Random (MLR) sequences. Martin-Löf randomness attempts to capture what it means for a sequence of bits to be "truly random". By contrast, CLTs do not make assertions about the behavior of a single random sequence, but only on the distributional behavior of a sequence of random variables. Semantically, we usually interpret CLTs as assertions about the collective behavior of infinitely many sequences. Yet, our intuition is that if a sequence of bits is "truly random", then it should provide a "source of randomness" for which CLT-type results should hold. We tackle this difficulty by using a sampling scheme that generates an infinite number of samples from a single binary sequence. We show that when we apply this scheme to a Martin-Löf random sequence, the empirical moments and cumulative density functions (CDF) of these samples tend to their corresponding counterparts for the normal distribution. We also prove the well known almost sure central limit theorem (ASCLT), which provides an alternative, albeit less intuitive, answer to this question. Both results are also generalized for Schnorr random sequences.

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