A Theory of Approximation for Stochastic Biochemical Processes
A Theory of Approximation for Stochastic Biochemical Processes
This chapter analyzes stochastic models, demonstrating a powerful technique for model comparison and model calibration. The method uses the mathematical construct known as the Wasserstein pseudometric and is general enough to address many modeling problems of interest, such as model comparison, parameter estimation, and model invalidation. This chapter concentrates on stochastic reaction networks. It shows that the Wasserstein pseudometric method proposed has been developed with an eye toward maximum generality. In principle, it can be employed with any class of stochastic model given enough time and enough sample data.
Keywords: stochastic models, model comparison, model calibration, Wasserstein pseudometric method, parameter estimation, model invalidation, stochastic reaction networks
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