58 citations · 148 across the 25 of their papers we have counts for
16 papers · 1 filter
Accelerating Bayesian inference for stochastic epidemic models using incidence data
Andrew Golightly, Laura E. Wadkin, Sam A. Whitaker +3
We consider the case of performing Bayesian inference for stochastic epidemic compartment models, using incomplete time course data consisting of incidence counts that are either t…
Accelerating inference for stochastic kinetic models
Tom E. Lowe, Andrew Golightly, Chris Sherlock
Stochastic kinetic models (SKMs) are increasingly used to account for the inherent stochasticity exhibited by interacting populations of species in areas such as epidemiology, popu…
Augmented pseudo-marginal Metropolis-Hastings for partially observed diffusion processes
Andrew Golightly, Chris Sherlock
We consider the problem of inference for nonlinear, multivariate diffusion processes, satisfying Itô stochastic differential equations (SDEs), using data at discrete times that may…
Exact Bayesian inference for discretely observed Markov Jump Processes using finite rate matrices
Chris Sherlock, Andrew Golightly
We present new methodologies for Bayesian inference on the rate parameters of a discretely observed continuous-time Markov jump processes with a countably infinite state space. The…
Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms
Samuel Wiqvist, Andrew Golightly, Ashleigh T. McLean +1
Stochastic differential equation mixed-effects models (SDEMEMs) are flexible hierarchical models that are able to account for random variability inherent in the underlying time-dyn…
Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter
Christopher Drovandi, Richard G Everitt, Andrew Golightly +1
Particle Markov chain Monte Carlo (pMCMC) is now a popular method for performing Bayesian statistical inference on challenging state space models (SSMs) with unknown static paramet…