ADM-CLE approach for detecting slow variables in continuous time Markov
  chains and dynamic data

Journal article

Abstract:: A method for detecting intrinsic slow variables in high-dimensional stochastic chemical reaction networks is developed and analyzed. It combines anisotropic diffusion maps (ADM) with approximations based on the chemical Langevin equation (CLE). The resulting approach, called ADM-CLE, has the potential of being more efficient than the ADM method for a large class of chemical reaction systems, because it replaces the computationally most expensive step of ADM (running local short bursts of simu...
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Role:

Author

Institution:

University of Oxford

Division:

MPLS

Department:

Mathematical Institute

Role:

Author

Keywords:: stochastic chemical reaction networks

slow variables

diffusion maps

stationary distributions
Pubs id:: pubs:517507
UUID:: uuid:cd167cf5-0b10-47bb-a5f5-71b14b8bb9b8
Local pid:: pubs:517507
Source identifiers:: 517507
Deposit date:: 2015-04-28

Copyright holder:: Cucuringu and Erban
Notes:: This paper has been submitted to SIAM Journal on Scientific Computing.

Licence:: Terms and Conditions of Use for Oxford University Research Archive

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