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Approximate Discrete-Time Filtering for Stochastic Chemical Kinetics

Author(s):

L. Studer
Conference/Journal:

Semester Thesis, HS 13 (10309)
Abstract:

Due to the analytical complexity of stochastic kinetic models, closed-form lters cannot be derived for all but the simplest scenarios. In this semester project, several lters based on moment dynamics were developed. Among them are lters based on Laplace's Method for integral approximation and the Unscented Kalman Filter. The performance of the approximated lters were benchmarked against exact lters obtained by Finite State Projection on synthetic data.

Year:

2014
Type of Publication:

(13)Semester/Bachelor Thesis
Supervisor:

C. Zechner

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% Autogenerated BibTeX entry
@PhdThesis { Xxx:2014:IFA_4659
}
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