Predicting the future : completing models of observed complex systems

Predicting the Future: Completing Models of Observed Complex Systems provides a general framework for the discussion of model building and validation across a broad spectrum of disciplines. This is accomplished through the development of an exact path integral for use in transferring information fro...

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Detaylı Bibliyografya
Yazar: Abarbanel, Henry Don Isaac, 1943-
Materyal Türü: Livre numérique
Dil:Anglais
Baskı/Yayın Bilgisi: New York, NY : Springer New York : Imprint: Springer [20..].
Cham : Springer Nature
Seri Bilgileri:Understanding Complex Systems
Online Erişim:Accès sur la plateforme de l'éditeur
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Accès Université d'Orléans
Accès INSA CVL
Not: Archives Springer e-books (Licence nationale)
Archives Springer e-books (Licence nationale)
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Edition sous un autre format:• Predicting the Future, Texte imprimé, 9781461472179
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100 1 |a Abarbanel, Henry Don Isaac,  |d 1943- 
245 1 0 |a Predicting the future :  |b completing models of observed complex systems   |c by Henry Abarbanel. 
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490 0 |a Understanding Complex Systems  |x 1860-0832 
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505 1 |a Preface 1 An Overview; The Challenge of Complex Systems 2 Examples as a Guide to the Issues 3 General Formulation of Statistical Data Assimilation 4 Evaluating the Path Integral 5 Twin Experiments 6 Analysis of Experimental Data 
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506 |a Conditions particulières de réutilisation pour les bénéficiaires des licences nationales. https://www.licencesnationales.fr/springer-nature-ebooks-contrat-licence-ln-2017 
520 |a Predicting the Future: Completing Models of Observed Complex Systems provides a general framework for the discussion of model building and validation across a broad spectrum of disciplines. This is accomplished through the development of an exact path integral for use in transferring information from observations to a model of the observed system. Through many illustrative examples drawn from models in neuroscience, fluid dynamics, geosciences, and nonlinear electrical circuits, the concepts are exemplified in detail. Practical numerical methods for approximate evaluations of the path integral are explored, and their use in designing experiments and determining a model's consistency with observations is investigated. Using highly instructive examples, the problems of data assimilation and the means to treat them are clearly illustrated. This book will be useful for students and practitioners of physics, neuroscience, regulatory networks, meteorology and climate science, network dynamics, fluid dynamics, and other systematic investigations of complex systems 
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