Staff profile

• Bayesian Statistics
• Simulation-based inference approaches e.g. MCMC, SMC
• Stochastic Kinetic Models
• Stochastic Differential Equations

• 2015: Editorial Duties: AE, Mathematical Biosciences (2015-2020)
• 2010: National and International Collaboration: C. Sherlock (Lancaster), U. Picchini (Chalmers, Sweden), T. Kypraios (Nottingham), A. Baggaley (Newcastle)
• 2009: Biennial RSS Research Prize: Awarded for work at the interface between Statistics and Systems Biology
• 2003: Membership of Professional Body: RSS fellow

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
• 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
• 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
• Wadkin, L. E., Holden, J., Ettelaie, R., Holmes, M. J., Smith, J., Golightly, A., Parker, N. G., & 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• 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
• Lai, Y., Golightly, A., & Boys, R. J. (2020). Sequential Bayesian inference for spatio-temporal models of temperature and humidity data. Journal of Computational Science, 43, https://doi.org/10.1016/j.jocs.2020.101125
• Golightly, A., Bradley, E., Lowe, T., & Gillespie, C. S. (2019). Correlated pseudo-marginal schemes for time-discretised stochastic kinetic models. Computational Statistics & Data Analysis, 136, https://doi.org/10.1016/j.csda.2019.01.006
• Gillespie, C. S., & Golightly, A. (2019). Guided proposals for efficient weighted stochastic simulation. The Journal of Chemical Physics, 150(22), https://doi.org/10.1063/1.5090979
• Golightly, A., & Sherlock, C. (2019). Efficient sampling of conditioned Markov jump processes. Statistics and Computing, 29(5), https://doi.org/10.1007/s11222-019-09861-5
• schemes for stochastic kinetic models. Statistics and Computing, 28(6), https://doi.org/10.1007/s11222-017-9789-8
• Forsyth, R., Young, D., Kelly, G., Davis, K., Dunford, C., Golightly, A., Marshall, L., & Wales, L. (2017). Paediatric Rehabilitation Ingredients Measure: a new tool for identifying paediatric neurorehabilitation content. Developmental Medicine & Child Neurology, 60(3), https://doi.org/10.1111/dmcn.13648
• Whitaker, G. A., Golightly, A., Boys, R. J., & Sherlock, C. (2017). Improved bridge constructs for stochastic differential equations. Statistics and Computing, 27(4), https://doi.org/10.1007/s11222-016-9660-3
• Sherlock, C., Golightly, A., & Henderson, D. A. (2017). Adaptive, Delayed-Acceptance MCMC for Targets With Expensive Likelihoods. Journal of Computational and Graphical Statistics, 26(2), https://doi.org/10.1080/10618600.2016.1231064
• Whitaker, G. A., Golightly, A., Boys, R. J., & Sherlock, C. (2017). Bayesian Inference for Diffusion-Driven Mixed-Effects Models. Bayesian Analysis, 12(2), https://doi.org/10.1214/16-ba1009
• Gillespie, C. S., & Golightly, A. (2016). Diagnostics for assessing the linear noise and moment closure approximations. Statistical Applications in Genetics and Molecular Biology, 15(5), https://doi.org/10.1515/sagmb-2014-0071
• Golightly, A., & Wilkinson, D. J. (2015). Bayesian inference for Markov jump processes with informative observations. Statistical Applications in Genetics and Molecular Biology, 14(2), https://doi.org/10.1515/sagmb-2014-0070
• Golightly, A., Henderson, D. A., & Sherlock, C. (2015). Delayed acceptance particle MCMC for exact inference in stochastic kinetic models. Statistics and Computing, 25(5), https://doi.org/10.1007/s11222-014-9469-x
• Henderson, D. A., Baggaley, A. W., Shukurov, A., Boys, R. J., Sarson, G. R., & Golightly, A. (2014). Regional variations in the European Neolithic dispersal: the role of the coastlines. Antiquity, 88(342), https://doi.org/10.1017/s0003598x00115467
• Sherlock, C., Golightly, A., & Gillespie, C. S. (2014). Bayesian inference for hybrid discrete-continuous stochastic kinetic models. Inverse Problems, 30(11), https://doi.org/10.1088/0266-5611/30/11/114005
• Golightly, A., Boys, R. J., Cameron, K. M., & Zglinicki, T. V. (2012). The effect of late onset, short-term caloric restriction on the core temperature and physical activity in mice. Journal of the Royal Statistical Society: Series C, 61(5), https://doi.org/10.1111/j.1467-9876.2012.01045.x
• Baggaley, A. W., Sarson, G. R., Shukurov, A., Boys, R. J., & Golightly, A. (2012). Bayesian inference for a wave-front model of the neolithization of Europe. Physical review E: Statistical, nonlinear, and soft matter physics, 86(1), https://doi.org/10.1103/physreve.86.016105
• Baggaley, A. W., Boys, R. J., Golightly, A., Sarson, G. R., & Shukurov, A. (2012). Inference for population dynamics in the Neolithic period. Annals of Applied Statistics, 6(4), https://doi.org/10.1214/12-aoas579
• Cameron, K. M., Golightly, A., Miwa, S., Speakman, J., Boys, R., & von Zglinicki, T. (2011). Gross energy metabolism in mice under late onset, short term caloric restriction. Mechanisms of Ageing and Development, 132(4), https://doi.org/10.1016/j.mad.2011.04.004
• Golightly, A., & Wilkinson, D. J. (2011). Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo. Interface Focus, 1(6), https://doi.org/10.1098/rsfs.2011.0047
• Gillespie, C. S., & Golightly, A. (2010). Bayesian inference for generalized stochastic population growth models with application to aphids. Journal of the Royal Statistical Society: Series C, 59(2), https://doi.org/10.1111/j.1467-9876.2009.00696.x
• Golightly, A. (2009). Bayesian Filtering for Jump-Diffusions With Application to Stochastic Volatility. Journal of Computational and Graphical Statistics, 18(2), https://doi.org/10.1198/jcgs.2009.07137
• Golightly, A., & Wilkinson, D. (2008). Bayesian inference for nonlinear multivariate diffusion models observed with error. Computational Statistics & Data Analysis, 52(3), https://doi.org/10.1016/j.csda.2007.05.019
• Golightly, A., & Wilkinson, D. J. (2006). Bayesian sequential inference for nonlinear multivariate diffusions. Statistics and Computing, 16(4), https://doi.org/10.1007/s11222-006-9392-x
• Golightly, A., & Wilkinson, D. J. (2006). Bayesian Sequential Inference for Stochastic Kinetic Biochemical Network Models. Journal of Computational Biology, 13(3), https://doi.org/10.1089/cmb.2006.13.838
• Golightly, A., & Wilkinson, D. (2005). Bayesian Inference for Stochastic Kinetic Models Using a Diffusion Approximation. Biometrics, 61(3), https://doi.org/10.1111/j.1541-0420.2005.00345.x