Weak noise approximate inference for diffusion models

author: Andreas Ruttor, Department of Software Engineering and Theoretical Computer Science, Faculty VI Electrical Engineering and Computer Sciences, TU Berlin
published: Nov. 6, 2007,   recorded: September 2007,   views: 3190
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Description

The modelling of the Stochastic Kinetics of biochemical networks by stochastic di erential equations (SDE) has been successfully used as a basis for statistical inference for such models. Since Monte Carlo based inference can be time consuming for SDEs, we suggest a di erent approximate approach. The idea is that a di usion model applies well to chemical kinetics, when the number of molecules of each type is large. In this limit, also the number fluctuations are small leading to a small di usion term compared to the drift. This suggests the application of a weak noise expansion.

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