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Weak noise approximate inference for diffusion models
This video was recorded at Workshop on Practical Inference Methods for Mechanistic Modelling of Biological Systems (PIMMS), Glasgow 2007. The modelling of the Stochastic Kinetics of biochemical networks by stochastic dierential 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 dierent approximate approach. The idea is that a diusion 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 diusion term compared to the drift. This suggests the application of a weak noise expansion.
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This work is licensed under a Attribution-NonCommercial-NoDerivs 3.0 United States
