Memory order decomposition of symbolic sequences.
Memory order decomposition of symbolic sequences.
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符号序列的记忆顺序分解。
DOI：
10.1103/physreve.104.014112
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发表时间：
2021
期刊：
影响因子：
--
通讯作者：
Alvarez-Rodriguez U
中科院分区：
文献类型：
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作者：
Alvarez-Rodriguez U
本文介绍了一种基于高阶马尔可夫分析的符号序列记忆研究的一般方法。最好地表示序列的马尔可夫过程被表示为最小阶数的矩阵的混合，使得能够定义所谓的记忆简档，其明确地反映了相关性的真实阶数。通过恢复可调合成序列的记忆轮廓验证了该方法的有效性。最后，我们扫描真实的数据和展示与实际的例子，我们的协议可以用来提取相关的随机特性的符号序列。
We introduce a general method for the study of memory in symbolic sequences based on higher-order Markov analysis. The Markov process that best represents a sequence is expressed as a mixture of matrices of minimal orders, enabling the definition of the so-called memory profile, which unambiguously reflects the true order of correlations. The method is validated by recovering the memory profiles of tunable synthetic sequences. Finally, we scan real data and showcase with practical examples how our protocol can be used to extract relevant stochastic properties of symbolic sequences.