Using model-based proposals for fast parameter inference on discrete state space, continuous-time Markov processes

C. M. Pooley*, S. C. Bishop, G. Marion

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

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.

Original languageEnglish
Article number20150225
Number of pages11
JournalJournal of the Royal Society. Interface
Volume12
Issue number107
DOIs
Publication statusPublished - 6 Jun 2015

Keywords / Materials (for Non-textual outputs)

  • Markov chain Monte Carlo
  • particle Markov chain Monte Carlo
  • epidemic
  • discrete state space
  • Markov process
  • Bayesian inference
  • CHAIN MONTE-CARLO
  • STOCHASTIC EPIDEMICS
  • BAYESIAN-INFERENCE
  • SEIR MODEL
  • SIMULATION

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