Analysis of stochastic time series in N dimensions in the presence of strong measurement noise
Abstract
An extension and generalization of a recently presented approach for the analysis of Langevin-type stochastic processes in the presence of strong measurement noise is presented. For a stochastic process in N dimensions which is superimposed with strong, exponentially correlated, Gaussian distributed, measurement noise it is possible to extract the strength and the correlation functions of the noise as well as polynomial approximations of the drift and diffusion functions of the underlying process.
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