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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autor principal: Abarbanel, Henry (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: New York, NY : Springer New York : Imprint: Springer, 2013.
Edición:1st ed. 2013.
Colección:Understanding Complex Systems,
Temas:
System theory.
Mathematical physics.
Computer simulation.
Neurosciences.
Complex Systems.
Theoretical, Mathematical and Computational Physics.
Computer Modelling.
Neuroscience.
Acceso en línea:Texto Completo

MARC

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245 1 0 |a Predicting the Future  |h [electronic resource] :  |b Completing Models of Observed Complex Systems /  |c by Henry Abarbanel. 
250 |a 1st ed. 2013. 
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300 |a XVI, 238 p. 97 illus., 91 illus. in color.  |b online resource. 
336 |a text  |b txt  |2 rdacontent 
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490 1 |a Understanding Complex Systems,  |x 1860-0840 
505 0 |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. 
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. 
650 0 |a System theory. 
650 0 |a Mathematical physics. 
650 0 |a Computer simulation. 
650 0 |a Neurosciences. 
650 1 4 |a Complex Systems. 
650 2 4 |a Theoretical, Mathematical and Computational Physics. 
650 2 4 |a Computer Modelling. 
650 2 4 |a Neuroscience. 
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830 0 |a Understanding Complex Systems,  |x 1860-0840 
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950 |a Physics and Astronomy (SpringerNature-11651) 
950 |a Physics and Astronomy (R0) (SpringerNature-43715) 

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