Proceedings of the National Academy of Sciences - PNAS, 2009-09, Vol.106 (38), p.16090-16095
2009
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Details
- Autor(en) / Beteiligte
- Titel
- Detecting intrinsic slow variables in stochastic dynamical systems by anisotropic diffusion maps
- Ist Teil von
- Proceedings of the National Academy of Sciences - PNAS, 2009-09, Vol.106 (38), p.16090-16095
- Ort / Verlag
- United States: National Academy of Sciences
- Erscheinungsjahr
- 2009
- Quelle
- MEDLINE
- Beschreibungen/Notizen
- 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.
- Sprache
- Englisch
- Identifikatoren
- ISSN: 0027-8424eISSN: 1091-6490DOI: 10.1073/pnas.0905547106Titel-ID: cdi_proquest_journals_201298673
- Format
- –
- Schlagworte
- Algorithms, Anisotropy, Chemical reactions, Computer Simulation, Coordinate systems, Covariance matrices, Datasets, Diffusion, Eigenvalues, Eigenvectors, Ellipses, Markov Chains, Models, Chemical, Molecules, Nonlinear Dynamics, Physical Sciences, Principal Component Analysis, Principal components analysis, Simulation, Stochastic models, Stochastic Processes, Time Factors, Trajectories