Journal article
Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps
- Abstract:
- 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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Bibliographic Details
- Publisher:
- PNAS
- Publication date:
- 2009-01-01
Item Description
- UUID:
-
uuid:ef96fcdc-09c6-4cf4-8b13-21fde18f3e22
- Local pid:
- oai:eprints.maths.ox.ac.uk:1637
- Deposit date:
- 2012-12-16
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- Copyright date:
- 2009
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