Accelerating inference for stochastic kinetic models

Lowe, Tom E. and Golightly, Andrew and Sherlock, Chris (2023) Accelerating inference for stochastic kinetic models. Computational Statistics and Data Analysis, 185: 107760. ISSN 0167-9473

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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 Itô 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.

Item Type:
Journal Article
Journal or Publication Title:
Computational Statistics and Data Analysis
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/1700/1703
Subjects:
?? stochastic kinetic modelmarkov jump processlinear noise approximationbayesian inferencedelayed acceptancemetropolis adjusted langevin algorithmcomputational theory and mathematicscomputational mathematicsapplied mathematicsstatistics and probability ??
ID Code:
191431
Deposited By:
Deposited On:
18 Apr 2023 10:55
Refereed?:
Yes
Published?:
Published
Last Modified:
15 Jul 2024 23:44