调整了Kolmogorov二进制单词的复杂性与经验度正常化
1Statistics Department, Texas A&M University, College Station, TX 77843, USA.
Entropy (Basel, Switzerland)
|February 27, 2026
概括
这项研究引入了对二进制单词的正常化复杂度测量,调整符号不平衡. 这种新的测量方法揭示了内在的复杂性及其随机序列的线性增长,连接了信息理论中的关键概念.
科学领域:
- 信息理论 信息理论
- 算法复杂性 算法复杂性
- 可能性理论概率理论.
背景情况:
- 科尔摩戈罗夫复杂度量化算法结构,但受到符号频率的影响.
- 具有不平衡符号分布的单词通常被认为在组合上不那么复杂.
- 现有的措施不能完全将内在的描述性复杂性与符号失衡隔离开来.
研究的目的:
- 引入对二进制单词的正常化复杂度测量.
- 从符号失衡的组合效应中分离内在的描述性复杂性.
- 探索科尔摩戈罗夫复杂性,经验和随机性之间的关系.
主要方法:
- 通过经验来规范科尔摩戈罗夫复杂性.
- 在构造性可交换措施下对马丁-洛夫随机序列进行调整复杂性的分析.
- 构建一种病理病例,以证明测量规律性的必要性.
主要成果:
- 拟议的正常化复杂性测量有效地将内在复杂性与符号失衡分开.
- 对于马丁-洛夫随机序列,调整后的复杂度呈现线性增长,并汇聚到1.
- 基本指标的正规性对于调整后的复杂度所观察到的属性至关重要.
结论:
- 该框架提供了科尔摩戈罗夫复杂性,实证和随机性之间的自然联系.
- 调整后的复杂度度为随机测试和结构化二进制数据分析提供了潜在的应用.
- 这项工作通过考虑符号分布来完善对二进制序列复杂性的理解.
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