title: Using model-based proposals for fast parameter inference on discrete state space, continuous-time Markov processes.
abstract: Bayesian statistics provides a framework for the integration of dynamic models with incomplete data to enable inference of model parameters and unobserved aspects of the system under study. An important class of dynamic models is discrete state space, continuous-time Markov processes (DCTMPs). Simulated via the Doob-Gillespie algorithm, these have been used to model systems ranging from chemistry to ecology to epidemiology. A new type of proposal, termed 'model-based proposal' (MBP), is developed for the efficient implementation of Bayesian inference in DCTMPs using Markov chain Monte Carlo (MCMC). This new method, which in principle can be applied to any DCTMP, is compared (using simple epidemiological SIS and SIR models as easy to follow exemplars) to a standard MCMC approach and a recently proposed particle MCMC (PMCMC) technique. When measurements are made on a single-state variable (e.g. the number of infected individuals in a population during an epidemic), model-based proposal MCMC (MBP-MCMC) is marginally faster than PMCMC (by a factor of 2-8 for the tests performed), and significantly faster than the standard MCMC scheme (by a factor of 400 at least). However, when model complexity increases and measurements are made on more than one state variable (e.g. simultaneously on the number of infected individuals in spatially separated subpopulations), MBP-MCMC is significantly faster than PMCMC (more than 100-fold for just four subpopulations) and this difference becomes increasingly large.
authors.affiliation: The Roslin Institute, The University of Edinburgh, Midlothian EH25 9RG, UK christopher.pooley@roslin.ed.ac.uk.
authors.forename: C M
authors.initials: CM
authors.lastname: Pooley
authors.affiliation: The Roslin Institute, The University of Edinburgh, Midlothian EH25 9RG, UK.
authors.forename: S C
authors.initials: SC
authors.lastname: Bishop
authors.affiliation: Biomathematics and Statistics Scotland, James Clerk Maxwell Building, The King's Buildings, Mayfield Road, Edinburgh EH9 3JZ, UK.
authors.forename: G
authors.initials: G
authors.lastname: Marion
mesh_terms: D008390:Markov Chains; D008962:Models, Theoretical
keywords: Bayesian inference; Markov chain Monte Carlo; Markov process; discrete state space; epidemic; particle Markov chain Monte Carlo