recently, a great deal of progress has been made in the modeling and understanding of processes with nonlinear dynamics, even when only time series data are available. modern reconstruction theory deals with creating nonlinear dynamical models from data and is at the heart of this improved understanding. most of the work has been done by dynamicists, but for the subject to reach maturity, statisticians and signal processing engineers need to provide input both to the theory and to the practice. the book brings together different approaches to nonlinear time series analysis in order to begin a synthesis that will lead to better theory and practice in all the related areas. 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 books 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.
