This book describes the state of the art in nonlinear dynamical reconstruction theory. The chapters are based upon a workshop held at the Isaac Newton Institute, Cambridge University, UK, in late 1998. The book's chapters present theory and methods topics by leading researchers in applied and theoretical nonlinear dynamics, statistics, probability, and systems theory.
Features and topics:
* disentangling uncertainty and error: the predictability of nonlinear systems
* achieving good nonlinear models
* delay reconstructions: dynamics vs. statistics
* introduction to Monte Carlo Methods for Bayesian Data Analysis
* latest results in extracting dynamical behavior via Markov Models
* data compression, dynamics and stationarity
Professionals, researchers, and advanced graduates in nonlinear dynamics, probability, optimization, and systems theory will find the book a useful resource and guide to current developments in the subject.
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