Predicting the Future - Completing Models of Observed Complex Systems

Through the development of an exact path integral for use in transferring information from observations to a model of the observed system, the author provides a general framework for the discussion of model building and evaluation across disciplines. Through many illustrative examples drawn from models in neuroscience, 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 explored.

Sommario

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.

Info autore

Henry Abarbanel is a new member of the Springer Complexity Board. He is a Professor of Physics at UCSD in La Jolla, CA. http: //neurograd.ucsd.edu/faculty/detail.php?id=1

Riassunto

Through the development of an exact path integral for use in transferring information from observations to a model of the observed system, the author provides a general framework for the discussion of model building and evaluation across disciplines.

Dettagli sul prodotto

• Autori: Henry Abarbanel, Henry D. I. Abarbanel, Henry D.I. Abarbanel
• Editore: Springer, Berlin
• Lingue: Inglese
• Formato: Copertina rigida
• Pubblicazione: 27.02.2013
• EAN: 9781461472179
• ISBN: 978-1-4614-7217-9
• Pagine: 238
• Dimensioni: 160 mm x 20 mm x 243 mm
• Peso: 542 g
• Illustrazioni: XVI, 238 p. 97 illus., 91 illus. in color.
• Serie: Understanding Complex Systems; Understanding Complex Systems
• Categoria: Scienze naturali, medicina, informatica, tecnica > Fisica, astronomia > Fisica teorica