Accelerating inference for stochastic kinetic models

TL;DR

This paper introduces a novel method to accelerate Bayesian inference for stochastic kinetic models by using a surrogate to improve efficiency of particle MCMC, enabling faster analysis of complex biological systems.

Contribution

It proposes a surrogate-based approach to enhance the efficiency of particle MCMC for stochastic kinetic models, addressing computational challenges in Bayesian inference.

Findings

Significant reduction in computational time compared to standard particle MCMC.

Maintains exactness in targeting the posterior distribution.

Applicable to models in epidemiology, ecology, and systems biology.

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 It\^o 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.