Structure and Randomness of Continuous-Time, Discrete-Event Processes

被引：22

作者：

Marzen, Sarah E. ^{[1
,2
]}

Crutchfield, James P. ^{[3
]}

机构：

[1] MIT, Dept Phys, Phys Living Syst Grp, Cambridge, MA 02139 USA

[2] Univ Calif Berkeley, Dept Phys, Berkeley, CA 94720 USA

[3] Univ Calif Davis, Dept Phys, Complex Sci Ctr, One Shields Ave, Davis, CA 95616 USA

来源：

JOURNAL OF STATISTICAL PHYSICS | 2017年 / 169卷 / 02期

基金：

美国国家科学基金会;

关键词：

Epsilon-machines; Causal states; Entropy rate; Statistical complexity; Hidden Markov processes; COMPLEXITY; ORDER;

D O I：

10.1007/s10955-017-1859-y

中图分类号：

O4 [物理学];

学科分类号：

0702 ;

摘要：

Loosely speaking, the Shannon entropy rate is used to gauge a stochastic process' intrinsic randomness; the statistical complexity gives the cost of predicting the process. We calculate, for the first time, the entropy rate and statistical complexity of stochastic processes generated by finite unifilar hidden semi-Markov models-memoryful, state-dependent versions of renewal processes. Calculating these quantities requires introducing novel mathematical objects (-machines of hidden semi-Markov processes) and new information-theoretic methods to stochastic processes.

引用

页码：303 / 315

页数：13

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