Limiting behaviour of pattern counts in biased binary strings

For $p \in (0,1)$, sample a binary sequence from the infinite product measure of Bernoulli$(p)$ distributions. It is known that for $p=1/2$, almost every binary sequence is Poisson generic in the sense of Peres and Weiss, a property that reflects a specific statistical pattern in the frequency of finite substrings. However, this behaviour is highly exceptional: it fails for any $p \ne 1/2$. In these other cases, we show that the frequency of substrings of almost every sequence has either trivial or peculiar behaviour. Nevertheless, the Poisson limiting regime can be recovered if one restricts attention to substrings with a fixed number of successes in the Bernoulli$(p)$ trials.

Publication Details

Journal
ArXiv.org
Published
2025-09-29
Primary Topic
Seismic Imaging and Inversion Techniques
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article
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article

Limiting behaviour of pattern counts in biased binary strings

ArXiv.org
Seismic Imaging and Inversion Techniques
article

Limiting behaviour of pattern counts in biased binary strings

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Abstract

For $p \in (0,1)$, sample a binary sequence from the infinite product measure of Bernoulli$(p)$ distributions. It is known that for $p=1/2$, almost every binary sequence is Poisson generic in the sense of Peres and Weiss, a property that reflects a specific statistical pattern in the frequency of finite substrings. However, this behaviour is highly exceptional: it fails for any $p \ne 1/2$. In these other cases, we show that the frequency of substrings of almost every sequence has either trivial or peculiar behaviour. Nevertheless, the Poisson limiting regime can be recovered if one restricts attention to substrings with a fixed number of successes in the Bernoulli$(p)$ trials.

ArXiv.org
Openalex Percentile: Top 40%
Seismic Imaging and Inversion Techniques
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Limiting behaviour of pattern counts in biased binary strings · ArXiv.org (2025) | TGRS Research Map | TGRS