Structure and Randomness of Continuous-Time, Discrete-Event Processes
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10955_2017_1859_ReferencePDF.pdf
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e856397e81b51ce7062c8442fa829186
Author(s)
Crutchfield, James P
Marzen, Sarah E.
Date Issued
August 2017
Journal
Journal of Statistical Physics
Publisher
Springer-Verlag
Citation
Marzen, Sarah E., and Crutchfield, James P. “Structure and Randomness of Continuous-Time, Discrete-Event Processes.” Journal of Statistical Physics 169, 2 (August 2017): 303–315 © 2017 Springer Science+Business Media, LLC
Version
Author's final manuscript
Abstract
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 (ϵ-machines of hidden semi-Markov processes) and new information-theoretic methods to stochastic processes.
MIT Department
Massachusetts Institute of Technology. Department of Physics
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1007/s10955-017-1859-y
