Informational and Causal Architecture of Discrete-Time Renewal Processes

Entropy

Sarah E. Marzen¹  James P. Crutchfield² 

[1] Department of Physics, University of California at Berkeley, Berkeley, CA 94720-5800, USA;Complexity Sciences Center and Department of Physics, University of California at Davis, One Shields Avenue, Davis, CA 95616, USA

关键词: stationary renewal process;    statistical complexity;    predictable information;    information anatomy;    entropy rate;   

DOI  :  10.3390/e17074891

来源: mdpi

【 摘 要 】

Renewal processes are broadly used to model stochastic behavior consisting of isolated events separated by periods of quiescence, whose durations are specified by a given probability law. Here, we identify the minimal sufficient statistic for their prediction (the set of causal states), calculate the historical memory capacity required to store those states (statistical complexity), delineate what information is predictable (excess entropy), and decompose the entropy of a single measurement into that shared with the past, future, or both. The causal state equivalence relation defines a new subclass of renewal processes with a finite number of causal states despite having an unbounded interevent count distribution. We use the resulting formulae to analyze the output of the parametrized Simple Nonunifilar Source, generated by a simple two-state hidden Markov model, but with an infinite-state ϵ-machine presentation. All in all, the results lay the groundwork for analyzing more complex processes with infinite statistical complexity and infinite excess entropy.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland