Skip to main content
Overview

Andrew Golightly

Associate Professor

MMathStat, PhD Newcastle University


Affiliations
AffiliationRoom numberTelephone
Associate Professor in the Department of Mathematical SciencesMCS3113+44 (0) 191 33 46228

Research interests

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

Esteem Indicators

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

Publications

Journal Article

  • Golightly, Andrew & Sherlock, Chris (2022). Augmented pseudo-marginal Metropolis-Hastings for partially observed diffusion processes. Statistics and Computing 32: 21.
  • Wadkin, Laura E., Branson, Julia, Hoppit, Andrew, Parker, Nick G., Golightly, Andrew & Baggaley, Andrew W. (2022). Inference for epidemic models with time varying infection rates: tracking the dynamics of oak processionary moth in the UK. Ecology and Evolution
  • Sherlock, Chris, Thiery, Alexandre H. & Golightly, Andrew (2021). Efficiency of delayed-acceptance random walk Metropolis algorithms. The Annals of Statistics 49(5): 2972-2990.
  • Fisher, H. F., Boys, R. J., Gillespie, C. S., Proctor, C. J. & Golightly, A. (2021). Parameter inference for a stochastic kinetic model of expanded polyglutamine proteins. Biometrics
  • Wiqvist, Samuel, Golightly, Andrew, McLean, Ashleigh T. & Picchini, Umberto (2021). Efficient inference for stochastic differential equation mixed-effects models using correlated particle pseudo-marginal algorithms. Computational Statistics & Data Analysis 157: 107151.
  • Drovandi, Christopher, Everitt, Richard G., Golightly, Andrew & Prangle, Dennis (2022). Ensemble MCMC: Accelerating Pseudo-Marginal MCMC for State Space Models using the Ensemble Kalman Filter. Bayesian Analysis 17(1): 223-260.
  • Lai, Yingying, Golightly, Andrew & Boys, Richard J. (2020). Sequential Bayesian inference for spatio-temporal models of temperature and humidity data. Journal of Computational Science 43: 101125.
  • Golightly, Andrew, Bradley, Emma, Lowe, Tom & Gillespie, Colin S. (2019). Correlated pseudo-marginal schemes for time-discretised stochastic kinetic models. Computational Statistics & Data Analysis 136: 92.
  • Gillespie, Colin S. & Golightly, Andrew (2019). Guided proposals for efficient weighted stochastic simulation. The Journal of Chemical Physics 150(22): 224103.
  • Golightly, Andrew & Sherlock, Chris (2019). Efficient sampling of conditioned Markov jump processes. Statistics and Computing 29(5): 1149.
  • Golightly, Andrew & Kypraios, Theodore (2018). Efficient $$\hbox {SMC}^2$$ SMC 2 schemes for stochastic kinetic models. Statistics and Computing 28(6): 1215.
  • Forsyth, Rob, Young, David, Kelly, Gemma, Davis, Kathy, Dunford, Carolyn, Golightly, Andrew, Marshall, Lindsay & Wales, Lorna (2017). Paediatric Rehabilitation Ingredients Measure: a new tool for identifying paediatric neurorehabilitation content. Developmental Medicine & Child Neurology 60(3): 299.
  • Sherlock, Chris, Golightly, Andrew & Henderson, Daniel A. (2017). Adaptive, Delayed-Acceptance MCMC for Targets With Expensive Likelihoods. Journal of Computational and Graphical Statistics 26(2): 434.
  • Whitaker, Gavin A., Golightly, Andrew, Boys, Richard J. & Sherlock, Chris (2017). Bayesian Inference for Diffusion-Driven Mixed-Effects Models. Bayesian Analysis 12(2).
  • Whitaker, Gavin A., Golightly, Andrew, Boys, Richard J. & Sherlock, Chris (2017). Improved bridge constructs for stochastic differential equations. Statistics and Computing 27(4): 885.
  • Gillespie, Colin S. & Golightly, Andrew (2016). Diagnostics for assessing the linear noise and moment closure approximations. Statistical Applications in Genetics and Molecular Biology 15(5).
  • Golightly, Andrew, Henderson, Daniel A. & Sherlock, Chris (2015). Delayed acceptance particle MCMC for exact inference in stochastic kinetic models. Statistics and Computing 25(5): 1039.
  • Golightly, Andrew & Wilkinson, Darren J. (2015). Bayesian inference for Markov jump processes with informative observations. Statistical Applications in Genetics and Molecular Biology 14(2).
  • Sherlock, Chris, Golightly, Andrew & Gillespie, Colin S (2014). Bayesian inference for hybrid discrete-continuous stochastic kinetic models. Inverse Problems 30(11): 114005.
  • Henderson, Daniel A., Baggaley, Andrew W., Shukurov, Anvar, Boys, Richard J., Sarson, Graeme R. & Golightly, Andrew (2014). Regional variations in the European Neolithic dispersal: the role of the coastlines. Antiquity 88(342): 1291.
  • Baggaley, Andrew W., Boys, Richard J., Golightly, Andrew, Sarson, Graeme R. & Shukurov, Anvar (2012). Inference for population dynamics in the Neolithic period. The Annals of Applied Statistics 6(4).
  • Golightly, Andrew, Boys, Richard J., Cameron, Kerry M. & Zglinicki, Thomas von (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 (Applied Statistics) 61(5): 733.
  • Baggaley, Andrew W., Sarson, Graeme R., Shukurov, Anvar, Boys, Richard J. & Golightly, Andrew (2012). Bayesian inference for a wave-front model of the neolithization of Europe. Physical Review E 86(1).
  • Golightly, Andrew & Wilkinson, Darren J. (2011). Bayesian parameter inference for stochastic biochemical network models using particle Markov chain Monte Carlo. Interface Focus 1(6): 807.
  • Cameron, Kerry M., Golightly, Andrew, Miwa, Satomi, Speakman, John, Boys, Richard & von Zglinicki, Thomas (2011). Gross energy metabolism in mice under late onset, short term caloric restriction. Mechanisms of Ageing and Development 132(4): 202.
  • Gillespie, Colin S. & Golightly, Andrew (2010). Bayesian inference for generalized stochastic population growth models with application to aphids. Journal of the Royal Statistical Society: Series C (Applied Statistics) 59(2): 341.
  • Golightly, Andrew (2009). Bayesian Filtering for Jump-Diffusions With Application to Stochastic Volatility. Journal of Computational and Graphical Statistics 18(2): 384.
  • Golightly, A. & Wilkinson, D.J. (2008). Bayesian inference for nonlinear multivariate diffusion models observed with error. Computational Statistics & Data Analysis 52(3): 1674.
  • Golightly, Andrew & Wilkinson, Darren J. (2006). Bayesian Sequential Inference for Stochastic Kinetic Biochemical Network Models. Journal of Computational Biology 13(3): 838.
  • Golightly, Andrew & Wilkinson, Darren J. (2006). Bayesian sequential inference for nonlinear multivariate diffusions. Statistics and Computing 16(4): 323.
  • Golightly, A. & Wilkinson, D. J. (2005). Bayesian Inference for Stochastic Kinetic Models Using a Diffusion Approximation. Biometrics 61(3): 781.