Markov chain Monte Carlo inference for Markov jump processes via the linear noise approximation
Year: 2013
Type: article
Abstract: Bayesian analysis for Markov jump processes (MJPs) is a non-trivial and challenging problem. Although exact inference is theoretically possible, it is computationally demanding, thus its applicability is limited to a small class of problems. In this paper, we describe the application of Riemann manifold Markov chain Monte Carlo (MCMC) methods using an approximation to the likelihood of the MJP that is valid when the system modelled is near its thermodynamic limit. The proposed approach is both statistically and computationally efficient whereas the convergence rate and mixing of the chains allow for fast MCMC inference. The methodology is evaluated using numerical simulations on two problems from chemical kinetics and one from systems biology.
Authors: Vassilios Stathopoulos, Mark A. Girolami
Institution: University College London
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
Awards: G2117197975, Advancing Machine Learning Methodology for New Classes of Prediction Problems, Inference-based Modelling in Population and Systems Biology, G3389271054, G3549489554, Advancing the Geometric Framework for Computational Statistics: Theory, Methodology and Modern Day Applications, G4763851349, G5594148631, G6479667774
OpenAlex ID: W2053644777