Sangam: A Confluence of Knowledge Streams

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

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dc.contributor Massachusetts Institute of Technology. Department of Physics
dc.contributor Marzen, Sarah E.
dc.creator Crutchfield, James P
dc.creator Marzen, Sarah E.
dc.date 2017-09-25T18:08:22Z
dc.date 2018-06-03T05:00:09Z
dc.date 2017-08
dc.date 2017-04
dc.date 2017-09-23T04:44:13Z
dc.date.accessioned 2023-03-01T18:08:04Z
dc.date.available 2023-03-01T18:08:04Z
dc.identifier 0022-4715
dc.identifier 1572-9613
dc.identifier http://hdl.handle.net/1721.1/111635
dc.identifier 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
dc.identifier.uri http://localhost:8080/xmlui/handle/CUHPOERS/278878
dc.description 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.
dc.format application/pdf
dc.language en
dc.publisher Springer-Verlag
dc.relation http://dx.doi.org/10.1007/s10955-017-1859-y
dc.relation Journal of Statistical Physics
dc.rights 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.
dc.rights Springer Science+Business Media, LLC
dc.source Springer US
dc.title Structure and Randomness of Continuous-Time, Discrete-Event Processes
dc.type Article
dc.type http://purl.org/eprint/type/JournalArticle


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