期刊
BIOMETRICS
卷 61, 期 3, 页码 781-788出版社
WILEY
DOI: 10.1111/j.1541-0420.2005.00345.x
关键词
Bayesian inference; Markov chain Monte Carlo; missing data; nonlinear diffusion; stochastic differential equation
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.
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