Roles and Responsibilities

Director of Postgraduate Studies (2020--)

Associate Editor, Mathematical Biosciences (2015--)

Memberships

Member of the Royal Statistical Society

Fellow of the Higher Education Academy

Research Interests

Google Scholar

Bayesian Statistics, stochastic kinetic models, stochastic differential equations, sequential Monte Carlo, Markov chain Monte Carlo

Postgraduate Supervision

Current:

Nicola Hewett, Md. Hossian, Tom Lowe, Max Piotrowicz, Matthew Robinson, Tom Ryder, Sam Whitaker

Past:

Ash McLean, Aamir Khan, Yingying Lai, Gavin Whitaker

Research Grants

ATI funded project, Streaming data modelling for realtime monitoring and forecasting, (10/2018 -- 09/2020), CoI

EPSRC IAA funded project, Efficient parameter estimation for quantitative systems pharmacology, (11/2018 -- 06/2019), CoI

Leverhulme grant F/00 125/AD, Mathematical Models for the developed Neolithic, (01/01/10 -- 01/07/13), CoI

Esteem Indicators

Royal Statistical Society Research Prize 2009

Undergraduate Teaching

MAS8951 Modern Bayesian Inference

MAS3914 Stochastic Financial Modelling

MAS3326/8326 Discrete Stochastic Modelling

MMathStat projects: Lane Stephenson, Elizabeth Goodall, Matthew Upton, Josh Rushton-Crawshaw, Andrew Robson, Tim English, Emma Bradley, Joel Groves, Will Seed, Na Eun Kim  

Postgraduate Teaching

Newcastle & Durham Graduate Course

• Drovandi C, Everitt RG, Golightly A, Prangle D. Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter. Bayesian Analysis 2021, epub ahead of print.
• Lai Y, Golightly A, Boys RJ. Sequential Bayesian inference for spatio-temporal models of temperature and humidity data. Journal of Computational Science 2020, 43, 101125.
• Golightly A, Bradley E, Lowe T, Gillespie CS. Correlated pseudo-marginal schemes for time-discretised stochastic kinetic models. Computational Statistics and Data Analysis 2019, 136, 92-107.
• Golightly A, Sherlock C. Efficient sampling of conditioned Markov jump processes. Statistics and Computing 2019, 29, 1149-1163.
• Gillespie CS, Golightly A. Guided proposals for efficient weighted stochastic simulation. Journal of Chemical Physics 2019, 150(22), 224103.
• Ryder T, Golightly A, McGough AS, Prangle D. Black-box Variational Inference for Stochastic Differential Equations. In: 35th International Conference on Machine Learning (ICML 2018). 2018, Stockholm, Sweden: International Machine Learning Society.
• Golightly A, Kypraios K. Efficient SMC² schemes for stochastic kinetic models. Statistics and Computing 2018, 28(6), 1215-1230.
• Forsyth R, Young D, Kelly G, Davis K, Dunford C, Golightly A, Marshall L, Wales L. Paediatric Rehabilitation Ingredients Measure: a new tool for identifying paediatric neurorehabilitation content. Developmental Medicine and Child Neurology 2018, 60(3), 299-305.
• Whitaker GA, Golightly A, Boys RJ, Sherlock C. Improved bridge constructs for stochastic differential equations. Statistics and Computing 2017, 27(4), 885-900.
• Sherlock C, Golightly A, Henderson DA. Adaptive, delayed-acceptance MCMC for targets with expensive likelihoods. Journal of Computational and Graphical Statistics 2016, 26(2), 434-444.
• Whitaker GA, Golightly A, Boys RJ, Sherlock C. Bayesian Inference for Diffusion-Driven Mixed-Effects Models. Bayesian Analysis 2016, 12(2), 435-463.
• Gillespie CS, Golightly A. Diagnostics for assessing the linear noise and moment closure approximations. Statistical Applications in Genetics and Molecular Biology 2016, 15(5), 363–379.
• Golightly A, Wilkinson DJ. Bayesian inference for Markov jump processes with informative observations. Statistical Applications in Genetics and Molecular Biology 2015, 14(2), 169-188.
• Golightly A, Henderson DA, Sherlock C. Delayed acceptance particle MCMC for exact inference in stochastic kinetic models. Statistics and Computing 2015, 25(5), 1039-1055.
• Sherlock C, Golightly A, Gillespie CS. Bayesian inference for hybrid discrete-continuous stochastic kinetic models. Inverse Problems 2014, 30(11), 114005.
• Henderson DA, Baggaley AW, Shukurov A, Boys RJ, Sarson GR, Golightly A. Regional variations in the European Neolithic dispersal: the role of the coastlines. Antiquity 2014, 88(342), 1291-1302.
• Golighty A, Gillespie CS. Simulation of stochastic kinetic models. In: Schneider, M.V, ed. In-silico Systems Biology: A systems-based approach to understanding biological processes. Humana Press, 2013, pp.169-187.
• Baggaley AW, Sarson GR, Shukurov A, Boys RJ, Golightly A. Bayesian inference for a wave-front model of the neolithization of Europe. Physical Review E 2012, 86(1), 016105.
• Gillespie CS, Golightly A. Bayesian inference for the chemical master equation using approximate models. In: 9th International Workshop on Computational Systems Biology (WCSB). 2012, Ulm, Germany.
• Baggaley AW, Boys RJ, Golightly A, Sarson GR, Shukurov A. Inference for population dynamics in the Neolithic period. Annals of Applied Statistics 2012, 6(4), 1352-1376.
• Golightly A, Boys RJ, Cameron KM, von Zglinicki T. 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 (Applied Statistics) 2012, 61(5), 733-751.
• Golightly A, Wilkinson DJ. Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo. Interface Focus 2011, 1(6), 807-820.
• Golightly A, Boys RJ. Discussion to "Riemann manifold Langevin and Hamiltonian Monte Carlo methods" by Girolami and Calderhead. Journal of the Royal Statistical Society, Series B: Statistical Methodology 2011, 73(2), 181-182.
• Cameron KM, Golightly A, Miwa S, Speakman J, Boys R, von Zglinicki T. Gross energy metabolism in mice under late onset, short term caloric restriction. Mechanisms of Ageing and Development 2011, 132(4), 202-209.
• Gillespie CS, Golightly A. Bayesian inference for generalized stochastic population growth models with application to aphids. Journal of the Royal Statistical Society: Series C (Applied Statistics) 2010, 59(2), 341-357.
• Golightly A, Wilkinson DJ. Discussion to "Particle Markov chain Monte Carlo Methods" by Andrieu, Doucet and Holenstein. Journal of the Royal Statistical Society Series B: Statistical Methodology 2010, 72(3), 322-323.
• Wilkinson DJ, Golightly A. Markov chain Monte Carlo algorithms for SDE parameter estimation. In: Lawrence, ND; Girolami, M; Rattray, M; Sanguinetti, G, ed. Learning and Inference in Computational Systems Biology. London: MIT Press, 2010, pp.253-276.
• Golightly A. Bayesian Filtering for Jump-Diffusions With Application to Stochastic Volatility. Journal of Computational and Graphical Statistics 2009, 18(2), 384-400.
• Golightly A, Wilkinson D. Bayesian inference for nonlinear multivariate diffusion models observed with error. Computational Statistics & Data Analysis 2008, 52(3), 1674-1693.
• Golightly A, Wilkinson DJ. Bayesian sequential inference for nonlinear multivariate diffusions. Statistics and Computing 2006, 16(4), 323-338.
• Golightly A, Wilkinson DJ. Bayesian sequential inference for stochastic kinetic biochemical network models. Journal of Computational Biology 2006, 13(3), 838-851.
• Golightly A, Wilkinson DJ. Bayesian inference for stochastic kinetic models using a diffusion approximation. Biometrics 2005, 61(3), 781-894.