Bayesian inference for hybrid discrete-continuous stochastic kinetic models

Sherlock, Christopher and Golightly, Andrew and Gillespie, Colin (2014) Bayesian inference for hybrid discrete-continuous stochastic kinetic models. Inverse Problems, 30 (11). ISSN 0266-5611

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Abstract

We consider the problem of efficiently performing simulation and inference for stochastic kinetic models. Whilst it is possible to work directly with the resulting Markov jump process, computational cost can be prohibitive for networks of realistic size and complexity. In this paper, we consider an inference scheme based on a novel hybrid simulator that classifies reactions as either "fast" or "slow" with fast reactions evolving as a continuous Markov process whilst the remaining slow reaction occurrences are modelled through a Markov jump process with time dependent hazards. A linear noise approximation (LNA) of fast reaction dynamics is employed and slow reaction events are captured by exploiting the ability to solve the stochastic differential equation driving the LNA. This simulation procedure is used as a proposal mechanism inside a particle MCMC scheme, thus allowing Bayesian inference for the model parameters. We apply the scheme to a simple application and compare the output with an existing hybrid approach and also a scheme for performing inference for the underlying discrete stochastic model.

Item Type:
Journal Article
Journal or Publication Title:
Inverse Problems
Uncontrolled Keywords:
/dk/atira/pure/subjectarea/asjc/2600/2614
Subjects:
ID Code:
71711
Deposited By:
Deposited On:
11 Nov 2014 10:56
Refereed?:
Yes
Published?:
Published
Last Modified:
28 Oct 2020 03:31