Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/146526
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dc.contributor.authorYang, Chengranen_US
dc.contributor.authorBinder, Felix C.en_US
dc.contributor.authorGu, Mileen_US
dc.contributor.authorElliott, Thomas J.en_US
dc.date.accessioned2021-02-23T06:53:07Z-
dc.date.available2021-02-23T06:53:07Z-
dc.date.issued2020-
dc.identifier.citationYang, C., Binder, F. C., Gu, M., & Elliott, T. J. (2020). Measures of distinguishability between stochastic processes. Physical Review E, 101(6), 062137-. doi:10.1103/physreve.101.062137en_US
dc.identifier.issn2470-0045en_US
dc.identifier.urihttps://hdl.handle.net/10356/146526-
dc.description.abstractQuantifying how distinguishable two stochastic processes are is at the heart of many fields, such as machine learning and quantitative finance. While several measures have been proposed for this task, none have universal applicability and ease of use. In this article, we suggest a set of requirements for a well-behaved measure of process distinguishability. Moreover, we propose a family of measures, called divergence rates, that satisfy all of these requirements. Focusing on a particular member of this family—the coemission divergence rate—we show that it can be computed efficiently, behaves qualitatively similar to other commonly used measures in their regimes of applicability, and remains well behaved in scenarios where other measures break down.en_US
dc.description.sponsorshipMinistry of Education (MOE)en_US
dc.description.sponsorshipNational Research Foundation (NRF)en_US
dc.language.isoenen_US
dc.relationNRF-NRFF2016-02en_US
dc.relationMOE2017-T1-002-043en_US
dc.relation.ispartofPhysical Review Een_US
dc.rights© 2020 American Physical Society (APS). All rights reserved. This paper was published in Physical Review E and is made available with permission of American Physical Society (APS).en_US
dc.subjectScience::Physicsen_US
dc.titleMeasures of distinguishability between stochastic processesen_US
dc.typeJournal Articleen
dc.contributor.schoolSchool of Physical and Mathematical Sciencesen_US
dc.contributor.researchComplexity Instituteen_US
dc.identifier.doi10.1103/PhysRevE.101.062137-
dc.description.versionPublished versionen_US
dc.identifier.pmid32688504-
dc.identifier.scopus2-s2.0-85088351861-
dc.identifier.issue6en_US
dc.identifier.volume101en_US
dc.subject.keywordsStochastic Processesen_US
dc.subject.keywordsInformation Theoryen_US
dc.description.acknowledgementThis research is supported by the National Research Foundation (NRF). Singapore, under its NRFF Fellow programme (Award No. NRF-NRFF2016-02), the Lee Kuan Yew Endowment Fund (Postdoctoral Fellowship), Singapore Ministry of Education Tier 1 Grants No. MOE2017-T1-002-043 and No FQXi-RFP-1809 from the Foundational Questions Institute and Fetzer Franklin Fund (a donor-advised fund of Silicon Valley Community Foundation). F.C.B. acknowledges funding from the European Union's Horizon 2020 research and innovation programme under the Marie Skłodowska Curie Grant Agreement No. 801110 and the Austrian Federal Ministry of Education, Science, and Research (BMBWF). T.J.E., C.Y., and F.C.B. thank the Centre for Quantum Technologies for their hospitality. Any opinions, findings and conclusions or recommendations expressed in this material are those of the author(s) and do not reflect the views of National Research Foundation, Singapore.en_US
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