Finite Algebras and AI: From Matrix Semantics to Stochastic Local Search

Stachniak, Zbigniew

doi:10.1007/978-3-540-30210-0_2

Cited by 1 publication

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“…The seminal paper [8] presented a maximum entropy approach to nonmonotonic reasoning. In [16] Stachniak points out the link between universal algebras and AI with a special emphasis to nonmonotonic reasoning. There is a close connection between satisfiability and preferential matrix representation ideas.…”

Section: Introductionmentioning

confidence: 99%

Dynamics of the Fisher information metric

Calmet

2005

Phys. Rev. E

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Article (Published Version) http://sro.sussex.ac.uk Calmet, Xavier and Calmet, Jaques (2005) Dynamics of the Fisher information metric. Physical Review E, 71 (5). ISSN 1539-3755 This version is available from Sussex Research Online: http://sro.sussex.ac.uk/15301/ This document is made available in accordance with publisher policies and may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the URL above for details on accessing the published version. Copyright and reuse:Sussex Research Online is a digital repository of the research output of the University.Copyright and all moral rights to the version of the paper presented here belong to the individual author(s) and/or other copyright owners. To the extent reasonable and practicable, the material made available in SRO has been checked for eligibility before being made available.Copies of full text items generally can be reproduced, displayed or performed and given to third parties in any format or medium for personal research or study, educational, or not-for-profit purposes without prior permission or charge, provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. We present a method to generate probability distributions that correspond to metrics obeying partial differential equations generated by extremizing a functional J͓g ͑ i ͔͒, where g ͑ i ͒ is the Fisher metric. We postulate that this functional of the dynamical variable g ͑ i ͒ is stationary with respect to small variations of these variables. Our approach enables a dynamical approach to the Fisher information metric. It allows one to impose symmetries on a statistical system in a systematic way. Dynamics of the Fisher information metric

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Section: Introductionmentioning

confidence: 99%

Dynamics of the Fisher information metric

Calmet

2005

Phys. Rev. E

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Finite Algebras and AI: From Matrix Semantics to Stochastic Local Search

Cited by 1 publication

References 9 publications

Dynamics of the Fisher information metric

Dynamics of the Fisher information metric

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