An experimental speech to graphics system. - Andrew Golightly; Tony Smith (CHINZ, 2002)

Bayesian Sequential Inference for Stochastic Kinetic Biochemical Network Models. - Andrew Golightly; Darren J. Wilkinson (J. Comput. Biol., 2006)

Bayesian sequential inference for nonlinear multivariate diffusions. - Andrew Golightly; Darren J. Wilkinson (Stat. Comput., 2006)

Bayesian inference for nonlinear multivariate diffusion models observed with error. - Andrew Golightly; Darren J. Wilkinson (Comput. Stat. Data Anal., 2008)

Markov Chain Monte Carlo Algorithms for SDE Parameter Estimation. - Darren J. Wilkinson; Andrew Golightly (Learning and Inference in Computational Systems Biology, 2010)

Delayed acceptance particle MCMC for exact inference in stochastic kinetic models. - Andrew Golightly; Daniel A. Henderson; Chris Sherlock (Stat. Comput., 2015)

Improved bridge constructs for stochastic differential equations. - Gavin A. Whitaker; Andrew Golightly; Richard J. Boys; Chris Sherlock (Stat. Comput., 2017)

Black-Box Variational Inference for Stochastic Differential Equations. - Thomas Ryder; Andrew Golightly; A. Stephen McGough; Dennis Prangle (ICML, 2018)

Black-Box Autoregressive Density Estimation for State-Space Models. - Tom Ryder; Andrew Golightly; A. Stephen McGough; Dennis Prangle (CoRR, 2018)

Efficient SMC2 schemes for stochastic kinetic models. - Andrew Golightly; Theodore Kypraios (Stat. Comput., 2018)

Scalable approximate inference for state space models with normalising flows. - Tom Ryder; Andrew Golightly; Isaac Matthews; Dennis Prangle (CoRR, 2019)

Correlated pseudo-marginal schemes for time-discretised stochastic kinetic models. - Andrew Golightly; Emma Bradley; Tom E. Lowe; Colin S. Gillespie (Comput. Stat. Data Anal., 2019)

Efficient sampling of conditioned Markov jump processes. - Andrew Golightly; Chris Sherlock (Stat. Comput., 2019)

Sequential Bayesian inference for spatio-temporal models of temperature and humidity data. - Yingying Lai; Andrew Golightly; Richard J. Boys (J. Comput. Sci., 2020)

The neural moving average model for scalable variational inference of state space models. - Thomas Ryder; Dennis Prangle; Andrew Golightly; Isaac Matthews (UAI, 2021)

Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms. - Samuel Wiqvist; Andrew Golightly; Ashleigh T. McLean; Umberto Picchini (Comput. Stat. Data Anal., 2021)

Augmented pseudo-marginal Metropolis-Hastings for partially observed diffusion processes. - Andrew Golightly; Chris Sherlock (Stat. Comput., 2022)

A sparse Bayesian hierarchical vector autoregressive model for microbial dynamics in a wastewater treatment plant. - Naomi E. Hannaford; Sarah E. Heaps; Tom M. W. Nye; Thomas P. Curtis; Ben Allen; Andrew Golightly; Darren J. Wilkinson (Comput. Stat. Data Anal., 2023)

Exact Bayesian Inference for Discretely Observed Markov Jump Processes Using Finite Rate Matrices. - Chris Sherlock; Andrew Golightly (J. Comput. Graph. Stat., 2023)

Accelerating inference for stochastic kinetic models. - Tom E. Lowe; Andrew Golightly; Chris Sherlock (Comput. Stat. Data Anal., 2023)

Accelerating Bayesian inference for stochastic epidemic models using incidence data. - Andrew Golightly; Laura E. Wadkin; Sam A. Whitaker; Andrew W. Baggaley; Nick G. Parker; Theodore Kypraios (Stat. Comput., 2023)

Bayesian inference for a spatio-temporal model of road traffic collision data. - Nicola Hewett; Andrew Golightly; Lee Fawcett; Neil Thorpe (J. Comput. Sci., 2024)

Bayesian classification of OXPHOS deficient skeletal myofibres. - Jordan Childs; Tiago Bernardino Gomes; Amy E. Vincent; Andrew Golightly; Conor Lawless (PLoS Comput. Biol., 2025)