Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps.

@article{Singer2009DetectingIS,
  title={Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps.},
  author={Amit Singer and Radek Erban and Ioannis G. Kevrekidis and Ronald R. Coifman},
  journal={Proceedings of the National Academy of Sciences of the United States of America},
  year={2009},
  volume={106 38},
  pages={16090-5}
}
Nonlinear independent component analysis is combined with diffusion-map data analysis techniques to detect good observables in high-dimensional dynamic data. These detections are achieved by integrating local principal component analysis of simulation bursts by using eigenvectors of a Markov matrix describing anisotropic diffusion. The widely applicable procedure, a crucial step in model reduction approaches, is illustrated on stochastic chemical reaction network simulations. 
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Stochastic Processes in Physics and Chemistry (North-Holland, Amsterdam), 3rd edition

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