Renewal Model for Dependent Binary Sequences

被引:0
|
作者
Zamparo, Marco [1 ,2 ]
机构
[1] Univ Bari, Dipartimento Fis, Via Amendola 173, I-70126 Bari, Italy
[2] Ist Nazl Fis Nucl, Sez Bari, Via Amendola 173, I-70126 Bari, Italy
关键词
Dependent binary data; Stationary binary sequences; Inverse problems; Maximum entropy principle; Law of large numbers; Central limit theorem; INFORMATION-THEORY; MEMORY FUNCTIONS;
D O I
10.1007/s10955-022-02893-8
中图分类号
O4 [物理学];
学科分类号
0702 ;
摘要
We suggest to construct infinite stochastic binary sequences by associating one of the two symbols of the sequence with the renewal times of an underlying renewal process. Focusing on stationary binary sequences corresponding to delayed renewal processes, we investigate correlations and the ability of the model to implement a prescribed autocovariance structure, showing that a large variety of subexponential decay of correlations can be accounted for. In particular, robustness and efficiency of the method are tested by generating binary sequences with polynomial and stretched-exponential decay of correlations. Moreover, to justify the maximum entropy principle for model selection, an asymptotic equipartition property for typical sequences that naturally leads to the Shannon entropy of the waiting time distribution is demonstrated. To support the comparison of the theory with data, a law of large numbers and a central limit theorem are established for the time average of general observables.
引用
收藏
页数:34
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