On Bayesian Inference for Stochastic Kinetic Models using Diffusion Approximations

This paper is concerned with the Bayesian estimation of stochastic rate constants in the context of dynamic models of intra-cellular processes. The underlying discrete stochastic kinetic model is replaced by a diffusion approximation (or stochastic differential equation approach) where a white noise term models stochastic behaviour and the model is identified using equispaced time course data. The estimation framework involves the introduction of m−1 latent data points between every pair of observations. MCMC methods are then used to sample the posterior distribution of the latent process and the model parameters.

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