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Correlated pseudo-marginal schemes for time-discretised stochastic kinetic models

Andrew Golightly, Emma Bradley, Tom Lowe and Colin S. Gillespie

Computational Statistics & Data Analysis, 2019, vol. 136, issue C, 92-107

Abstract: The challenging problem of conducting fully Bayesian inference for the reaction rate constants governing stochastic kinetic models (SKMs) is considered. Given the challenges underlying this problem, the Markov jump process representation is routinely replaced by an approximation based on a suitable time-discretisation of the system of interest. Improving the accuracy of these schemes amounts to using an ever finer discretisation level, which in the context of the inference problem, requires integrating over the uncertainty in the process at a predetermined number of intermediate times between observations. Pseudo-marginal Metropolis–Hastings schemes are increasingly used, since for a given discretisation level, the observed data likelihood can be unbiasedly estimated using a particle filter. When observations are particularly informative, an auxiliary particle filter can be implemented, by propagating particles conditional on the next observation. Recent work in state-space settings has shown how the pseudo-marginal approach can be made much more efficient by correlating the underlying pseudo-random numbers used to form the estimate of likelihood at the current and proposed values of the unknown parameters. This approach is extended to the time-discretised SKM framework by correlating the innovations that drive the auxiliary particle filter. The resulting approach is found to offer substantial gains in efficiency over a standard implementation.

Keywords: Auxiliary particle filter (APF); Bayesian inference; Markov jump process (MJP); Poisson leap; Chemical Langevin equation; Particle MCMC (search for similar items in EconPapers)
Date: 2019
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