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Professor Andrew Golightly's Outputs (43)

Bayesian classification of OXPHOS deficient skeletal myofibres (2025)
Journal Article
Childs, J., Golightly, A., Gomes, T. B., Vincent, A. E., & Lawless, C. (2025). Bayesian classification of OXPHOS deficient skeletal myofibres. PLoS Computational Biology, 21(2), Article e1012770. https://doi.org/10.1371/journal.pcbi.1012770

Mitochondria are organelles in most human cells which release the energy required for cells to function. Oxidative phosphorylation (OXPHOS) is a key biochemical process within mitochondria required for energy production and requires a range of protei... Read More about Bayesian classification of OXPHOS deficient skeletal myofibres.

Towards Data-Conditional Simulation for ABC Inference in Stochastic Differential Equations (2024)
Journal Article
Jovanovski, P., Golightly, A., & Picchini, U. (online). Towards Data-Conditional Simulation for ABC Inference in Stochastic Differential Equations. Bayesian Analysis, https://doi.org/10.1214/24-ba1467

We develop a Bayesian inference method for the parameters of discretely-observed stochastic differential equations (SDEs). Inference is challenging for most SDEs, due to the analytical intractability of the likelihood function. Nevertheless, forward... Read More about Towards Data-Conditional Simulation for ABC Inference in Stochastic Differential Equations.

Bayesian inference for a spatio-temporal model of road traffic collision data (2024)
Journal Article
Hewett, N., Golightly, A., Fawcett, L., & Thorpe, N. (2024). Bayesian inference for a spatio-temporal model of road traffic collision data. Journal of Computational Science, 80, Article 102326. https://doi.org/10.1016/j.jocs.2024.102326

Improving road safety is hugely important with the number of deaths on the world’s roads remaining unacceptably high; an estimated 1.35 million people die each year (WHO, 2020). Current practice for treating collision hotspots is almost always reacti... Read More about Bayesian inference for a spatio-temporal model of road traffic collision data.

Using extreme value theory to evaluate the leading pedestrian interval road safety intervention (2024)
Journal Article
Hewett, N., Fawcett, L., Golightly, A., & Thorpe, N. (2024). Using extreme value theory to evaluate the leading pedestrian interval road safety intervention. Stat, 13(2), Article e676. https://doi.org/10.1002/sta4.676

Improving road safety is hugely important with the number of deaths on the world's roads remaining unacceptably high; an estimated 1.3 million people die each year as a result of road traffic collisions. Current practice for treating collision hotspo... Read More about Using extreme value theory to evaluate the leading pedestrian interval road safety intervention.

Estimating the reproduction number, R0, from individual-based models of tree disease spread (2024)
Journal Article
Wadkin, L. E., Holden, J., Ettelaie, R., Holmes, M. J., Smith, J., Golightly, A., …Baggaley, A. W. (2024). Estimating the reproduction number, R0, from individual-based models of tree disease spread. Ecological Modelling, 489, Article 110630. https://doi.org/10.1016/j.ecolmodel.2024.110630

Tree populations worldwide are facing an unprecedented threat from a variety of tree diseases and invasive pests. Their spread, exacerbated by increasing globalisation and climate change, has an enormous environmental, economic and social impact. Com... Read More about Estimating the reproduction number, R0, from individual-based models of tree disease spread.

Accelerating Bayesian inference for stochastic epidemic models using incidence data (2023)
Journal Article
Golightly, A., Wadkin, L. E., Whitaker, S. A., Baggaley, A. W., Parker, N. G., & Kypraios, T. (2023). Accelerating Bayesian inference for stochastic epidemic models using incidence data. Statistics and Computing, 33(6), Article 134. https://doi.org/10.1007/s11222-023-10311-6

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 the number of new infections or removals in time intervals of fixed len... Read More about Accelerating Bayesian inference for stochastic epidemic models using incidence data.

Accelerating inference for stochastic kinetic models (2023)
Journal Article
Lowe, T., Golightly, A., & Sherlock, C. (2023). Accelerating inference for stochastic kinetic models. Computational Statistics & Data Analysis, 185, Article 107760. https://doi.org/10.1016/j.csda.2023.107760

Stochastic kinetic models (SKMs) are increasingly used to account for the inherent stochasticity exhibited by interacting populations of species in areas such as epidemiology, population ecology and systems biology. Species numbers are modelled using... Read More about Accelerating inference for stochastic kinetic models.

Quantifying Invasive Pest Dynamics through Inference of a Two-Node Epidemic Network Model (2023)
Journal Article
Wadkin, L. E., Golightly, A., Branson, J., Hoppit, A., Parker, N. G., & Baggaley, A. W. (2023). Quantifying Invasive Pest Dynamics through Inference of a Two-Node Epidemic Network Model. Diversity, 15(4), Article 496. https://doi.org/10.3390/d15040496

Invasive woodland pests have substantial ecological, economic, and social impacts, harming biodiversity and ecosystem services. Mathematical modelling informed by Bayesian inference can deepen our understanding of the fundamental behaviours of invasi... Read More about Quantifying Invasive Pest Dynamics through Inference of a Two-Node Epidemic Network Model.

Predicting extreme rainfall in regional areas of Bangladesh: A Bayesian approach (2023)
Journal Article
Hossain, M., Fawcett, L., & Golightly, A. (2023). Predicting extreme rainfall in regional areas of Bangladesh: A Bayesian approach. Australasian Journal of Regional Studies, 29(1), 114-132

Extreme weather events are anticipated to become more common around the world and they impact yield volatility i.e. reduce food production. Given the changing nature of the world s climate, and the disproportionate effect this might have on developin... Read More about Predicting extreme rainfall in regional areas of Bangladesh: A Bayesian approach.

A sparse Bayesian hierarchical vector autoregressive model for microbial dynamics in a wastewater treatment plant (2022)
Journal Article
Hannaford, N., Heaps, S., Nye, T., Curtis, T., Allen, B., Golightly, A., & Wilkinson, D. (2023). A sparse Bayesian hierarchical vector autoregressive model for microbial dynamics in a wastewater treatment plant. Computational Statistics & Data Analysis, 179, https://doi.org/10.1016/j.csda.2022.107659

Proper function of a wastewater treatment plant (WWTP) relies on maintaining a delicate balance between a multitude of competing microorganisms. Gaining a detailed understanding of the complex network of interactions therein is essential to maximisin... Read More about A sparse Bayesian hierarchical vector autoregressive model for microbial dynamics in a wastewater treatment plant.

Exact Bayesian inference for discretely observed Markov Jump Processes using finite rate matrices (2022)
Journal Article
Sherlock, C., & Golightly, A. (2023). Exact Bayesian inference for discretely observed Markov Jump Processes using finite rate matrices. Journal of Computational and Graphical Statistics, 32(1), 36-48. https://doi.org/10.1080/10618600.2022.2093886

We present new methodologies for Bayesian inference on the rate parameters of a discretely observed continuous-time Markov jump process with a countably infinite statespace. The usual method of choice for inference, particle Markov chain Monte Carlo... Read More about Exact Bayesian inference for discretely observed Markov Jump Processes using finite rate matrices.

Inference for epidemic models with time varying infection rates: tracking the dynamics of oak processionary moth in the UK (2022)
Journal Article
Wadkin, L. E., Branson, J., Hoppit, A., Parker, N. G., Golightly, A., & Baggaley, A. W. (2022). Inference for epidemic models with time varying infection rates: tracking the dynamics of oak processionary moth in the UK. Ecology and Evolution, 12(5), Article e8871. https://doi.org/10.1002/ece3.8871

1. Invasive pests pose a great threat to forest, woodland, and urban tree ecosystems. The oak processionary moth (OPM) is a destructive pest of oak trees, first reported in the UK in 2006. Despite great efforts to contain the outbreak within the orig... Read More about Inference for epidemic models with time varying infection rates: tracking the dynamics of oak processionary moth in the UK.

Augmented pseudo-marginal Metropolis-Hastings for partially observed diffusion processes (2022)
Journal Article
Golightly, A., & Sherlock, C. (2022). Augmented pseudo-marginal Metropolis-Hastings for partially observed diffusion processes. Statistics and Computing, 32, Article 21. https://doi.org/10.1007/s11222-022-10083-5

We consider the problem of inference for nonlinear, multivariate diffusion processes, satisfying Itô stochastic differential equations (SDEs), using data at discrete times that may be incomplete and subject to measurement error. Our starting point is... Read More about Augmented pseudo-marginal Metropolis-Hastings for partially observed diffusion processes.

Efficiency of delayed-acceptance random walk Metropolis algorithms (2021)
Journal Article
Sherlock, C., Thiery, A. H., & Golightly, A. (2021). Efficiency of delayed-acceptance random walk Metropolis algorithms. Annals of Statistics, 49(5), 2972-2990. https://doi.org/10.1214/21-aos2068

Delayed-acceptance Metropolis–Hastings and delayed-acceptance pseudo-marginal Metropolis–Hastings algorithms can be applied when it is computationally expensive to calculate the true posterior or an unbiased stochastic approximation thereof, but a co... Read More about Efficiency of delayed-acceptance random walk Metropolis algorithms.

Parameter inference for a stochastic kinetic model of expanded polyglutamine proteins (2021)
Journal Article
Fisher, H., Boys, R., Gillespie, C., Proctor, C., & Golightly, A. (2022). Parameter inference for a stochastic kinetic model of expanded polyglutamine proteins. Biometrics, 78(3), 1195-1208. https://doi.org/10.1111/biom.13467

The presence of protein aggregates in cells is a known feature of many human age-related diseases, such as Huntington's disease. Simulations using fixed parameter values in a model of the dynamic evolution of expanded polyglutaime (PolyQ) proteins in... Read More about Parameter inference for a stochastic kinetic model of expanded polyglutamine proteins.

Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms (2020)
Journal Article
Wiqvist, S., Golightly, A., McLean, A. T., & Picchini, U. (2021). Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms. Computational Statistics & Data Analysis, 157, Article 107151. https://doi.org/10.1016/j.csda.2020.107151

Stochastic differential equation mixed-effects models (SDEMEMs) are flexible hierarchical models that are able to account for random variability inherent in the underlying time-dynamics, as well as the variability between experimental units and, opti... Read More about Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms.

Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter (2020)
Journal Article
Drovandi, C., Everitt, R. G., Golightly, A., & Prangle, D. (2022). Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter. Bayesian Analysis, 17(1), 223-260. https://doi.org/10.1214/20-ba1251

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 parameters. It uses a particle filter (PF) at each iteration of an MCMC algor... Read More about Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter.