166 citations · 615 across the 26 of their papers we have counts for
4 papers · 1 filter
Particle Metropolis-adjusted Langevin algorithms
Christopher Nemeth, Chris Sherlock, Paul Fearnhead
This paper proposes a new sampling scheme based on Langevin dynamics that is applicable within pseudo-marginal and particle Markov chain Monte Carlo algorithms. We investigate this…
Optimal scaling for the pseudo-marginal random walk Metropolis: insensitivity to the noise generating mechanism
Chris Sherlock
We examine the optimal scaling and the efficiency of the pseudo-marginal random walk Metropolis algorithm using a recently-derived result on the limiting efficiency as the dimensio…
Bayesian Inference for Hybrid Discrete-Continuous Stochastic Kinetic Models
Chris Sherlock, Andrew Golightly, Colin Gillespie
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 proc…
Delayed acceptance particle MCMC for exact inference in stochastic kinetic models
Andrew Golightly, Daniel A. Henderson, Chris Sherlock
Recently-proposed particle MCMC methods provide a flexible way of performing Bayesian inference for parameters governing stochastic kinetic models defined as Markov (jump) processe…