Titlebook: Nonlinear Dynamics and Statistics; Alistair I. Mees Book 2001 Springer Science+Business Media New York 2001 Monte Carlo method.Time series

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Disentangling Uncertainty and Error: On the Predictability of Nonlinear Systemsfore cast time. If a specified accuracy at a given future time is desired, a perfect model can specify the initial accuracy required to obtain it, and accountable ensemble forecasts can be obtained for each unknown initial condition. Statistics which reflect the global properties of infinitesimals,

完成才能戰(zhàn)勝

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黃油沒(méi)有

Some Remarks on the Statistical Modeling of Chaotic Systemsand understanding data from real-world dynamical systems. We present some new insights into the possibilities of obtaining consistent estimates for the invariants of a dynamical system, and some new results concerning noise-removal.

連鎖,連串

The Identification and Estimation of Nonlinear Stochastic Systemsso-called state-dependent parameters, and is able to handle non-stationarity, as long as the state-dependent parameters vary slowly compared to the significant dynamics. One of the main points made here is that most realistic systems have time-varying inputs which can be measured; models must take t

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APO

Constrained Randomization of Time Series for Nonlinearity Testsalized nonlinearity tests that use Monte Carlo resampling. We briefly discuss standard methods available for a limited range of problems. Then we put forth a novel scheme in which one can define arbitrary sets of observables and test if these observables give a complete account of the serial correla

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Removing the Noise from Chaos Plus Noiseered. It is shown that consistent signal extraction is impossible when the errors are distributed according to a density with unbounded support, and the underlying dynamical system admits bomoclinic pairs. It is also shown that consistent signal extraction is possible when the errors are uniformly b

AUGER

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