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Bayesian Inference for Stochastic Kinetic Models Using a Diffusion Approximation
Author(s) -
Golightly A.,
Wilkinson D. J.
Publication year - 2005
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/j.1541-0420.2005.00345.x
Subject(s) - stochastic differential equation , context (archaeology) , inference , mathematics , bayesian probability , bayesian inference , markov chain monte carlo , stochastic modelling , computer science , stochastic process , heavy traffic approximation , statistical physics , diffusion process , mathematical optimization , statistics , artificial intelligence , physics , paleontology , biology , knowledge management , innovation diffusion
Summary This article is concerned with the Bayesian estimation of stochastic rate constants in the context of dynamic models of intracellular 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 behavior 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. The methodology is applied to the estimation of parameters in a prokaryotic autoregulatory gene network.