Newcastle University

Qualifications

MMathStat, Newcastle University (2003)
PhD, Statistics, Newcastle University (2006)

Memberships

Member of the Royal Statistical Society

Fellow of the Higher Education Academy

Research Interests

Bayesian Statistics, diffusion processes, particle filters, Markov chain Monte Carlo

Postgraduate Supervision

Gavin Whittaker 

Research Grants

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

Projects

• Mechanisms of dietary restriction in mice – whole body physiology
  Project Leader(s): Thomas von Zglinicki

Undergraduate Teaching

MAS3312/8312 Stochastic Financial Modelling
MAS1403 Quantitative Methods for Business Management

MMathStat projects: Lane Stephenson, Elizabeth Goodall 

Postgraduate Teaching

Newcastle & Durham Graduate Course, every January

• 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. In Press.
• 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.