Accelerating inference for stochastic kinetic models

Lowe, T.E.; Golightly, A.; Sherlock, C.

Abstract

Stochastic kinetic models (SKMs) are increasingly used to account for the inherent stochasticity exhibited by interacting populations of species in areas such as epidemiology, population ecology and systems biology. Species numbers are modelled using a continuous-time stochastic process, and, depending on the application area of interest, this will typically take the form of a Markov jump process or an Itˆo diffusion process. Widespread use of these models is typically precluded by their computational complexity. In particular, performing exact fully Bayesian inference in either modelling framework is challenging due to the intractability of the observed data likelihood, necessitating the use of computationally intensive techniques such as particle Markov chain Monte Carlo (particle MCMC). It is proposed to increase the computational and statistical efficiency of this approach by leveraging the tractability of an inexpensive surrogate derived directly from either the jump or diffusion process. The surrogate is used in three ways: in the design of a gradient-based parameter proposal, to construct an appropriate bridge and in the first stage of a delayed-acceptance step. The resulting approach, which exactly targets the posterior of interest, offers substantial gains in efficiency over a standard particle MCMC implementation.

Citation

Lowe, T., Golightly, A., & Sherlock, C. (2023). Accelerating inference for stochastic kinetic models. Computational Statistics & Data Analysis, 185, Article 107760. https://doi.org/10.1016/j.csda.2023.107760

• Journal Article Type: Article
• Acceptance Date: Apr 6, 2023
• Online Publication Date: Apr 18, 2023
• Publication Date: 2023-09
• Deposit Date: Apr 10, 2023
• Publicly Available Date: May 16, 2023
• Journal: Computational Statistics and Data Analysis
• Print ISSN: 0167-9473
• Electronic ISSN: 1872-7352
• Publisher: Elsevier
• Peer Reviewed: Peer Reviewed
• Volume: 185
• Article Number: 107760
• DOI: https://doi.org/10.1016/j.csda.2023.107760
• Public URL: https://durham-repository.worktribe.com/output/1176267
• Related Public URLs: https://arxiv.org/abs/2206.02644