Cookies

We use cookies to ensure that we give you the best experience on our website. You can change your cookie settings at any time. Otherwise, we'll assume you're OK to continue.

Durham University

Email and Telephone Directory

Staff Profile

Ian Vernon, PhD University of Cambridge

Associate Professor, Statistics in the Department of Mathematical Sciences

Contact Ian Vernon (email at i.r.vernon@durham.ac.uk)

Research Groups

Department of Mathematical Sciences

  • Applied Mathematics: Biomathematics
  • Probability & Statistics: Statistics
  • Probability and Statistics

Research Interests

  • Bayesian Statistics
  • Bayes Linear Methods
  • Uncertainty Analysis of Computer Models of Complex Physical Systems
  • Galaxy Formation Simulations
  • Systems Biology Models
  • Epidemiology HIV Models
  • Geology models of oil reservoirs

Selected Publications

Chapter in book

  • Goldstein, M., Seheult, A. & Vernon, I. (2013). Assessing Model Adequacy. In Environmental Modelling: Finding Simplicity in Complexity. Mulligan, Mark. & Wainwright, John. Wiley-Blackwell. 435-449.

Journal Article

  • McKinley, T. J., Vernon, I., Andrianakis, I., McCreesh, N., Oakley, J. E., Nsubuga, R., Goldstein, M. & White, R. G. (2018). Approximate Bayesian Computation and simulation-based inference for complex stochastic epidemic models. Statistical Science 33(1): 4-18.
  • Vernon, Ian, Liu, Junli, Goldstein, Michael, Rowe, James, Topping, Jen & Lindsey, Keith (2018). Bayesian uncertainty analysis for complex systems biology models: emulation, global parameter searches and evaluation of gene functions. BMC Systems Biology 12: 1.
  • McCreesh, Nicky, Andrianakis, Ioannis, Nsubuga, Rebecca N., Strong, Mark, Vernon, Ian, McKinley, Trevelyan J., Oakley, Jeremy E., Goldstein, Michael, Hayes, Richard & White, Richard G. (2018). Choice of time horizon critical in estimating costs and effects of changes to HIV programmes. PLOS ONE 13(5): e0196480.
  • Moreno, R., Avansi, G., Schiozer, D., Vernon, I., Goldstein, M. & Caiado, C. (2018). Emulation of reservoir production forecast considering variation in petrophysical properties. Journal of Petroleum Science and Engineering 165: 711-725.
  • Formentin, Helena, Almeida, Forlan R Avansi, Guilherme D Maschio, Célio Schiozer, Denis J Caiado, Camila C , Vernon, Ian R & Goldstein, Michael (2018). Getting more from dynamic data series for the history matching process: A study using the UNISIM-I-H case. Journal of Petroleum Science and Engineering (in submission) PETROL13721.
  • Vernon, I. R., Jackson, S. E. & Cumming, J. A. (2018). Known Boundary Emulation of Complex Computer Models. SIAM/ASA Journal on Uncertainty Quantification (in submission)
  • Vernon, Ian (2018). Uncertainty Analysis for Heavy Simulations of Galaxy Formation. London Mathematical Society Newsletter 3(474): 28-33.
  • Vernon, I. & Gosling, J.P. (2017). Bayesian computer model analysis of Robust Bayesian analyses. Bayesian Analysis (in submission)
  • Jackson, Samuel E., Vernon, Ian, Liu, Junli & Lindsey, Keith (2017). Bayesian uncertainty analysis establishes the link between the parameter space of a complex model of hormonal crosstalk in Arabidopsis root development and experimental measurements. BMC Systems Biology (in submission)
  • Rodrigues, Luiz Felippe S., Vernon, Ian & Bower, Richard G. (2017). Constraints on galaxy formation models from the galaxy stellar mass function and its evolution. Monthly Notices of the Royal Astronomical Society 466(2): 2418-2435.
  • McCreesh, N., Andrianakis I., Nsubuga, R., Strong, M., Vernon, I., McKinley, T.J., Oakley, J.E., Goldstein, M., Hayes, R. & White, R.G. (2017). Costs and effects of changes to ART eligibility criteria in Uganda. PLoS ONE
  • Andrianakis, I., McCreesh, N., Vernon, I., McKinley, T. J., Oakley, J. E., Nsubuga, R., Goldstein, M. & White, R. G. (2017). Efficient History Matching of a High Dimensional Individual-Based HIV Transmission Model. SIAM/ASA Journal on Uncertainty Quantification 5(1): 694-719.
  • Andrianakis, I., Vernon, I., McCreesh, N., McKinley, T. J., Oakley, J. E., Nsubuga, R. N., Goldstein, M. & White, R. G. (2017). History matching of a complex epidemiological model of human immunodeficiency virus transmission by using variance emulation. Journal of the Royal Statistical Society: Series C (Applied Statistics) 66(4): 717-740.
  • McCreesh, N., Andrianakis, I., Nsubuga, R., Strong, M., Vernon, I., McKinley, T.J., Oakley, J.E., Goldstein, M., Hayes, R. & White, R.G. (2017). Improving ART programme retention and viral suppression are key to maximising impact of treatment as prevention – a modelling study. BMC Infectious Diseases 17(1): 557.
  • McCreesh, N., Andrianakis, I., Nsubuga, R., Strong, M., Vernon, I., McKinley, T.J. Oakley, J.E., Goldstein, M., Hayes, R. & White, R.G. (2017). Universal test, treat, and keep: improving ART retention is key in cost-effective HIV control in Uganda. BMC Infectious Diseases 17: 322.
  • Andrianakis, Ioannis, Vernon, Ian, McCreesh, N., McKinley, T.J., Oakley, J.E., Nsubuga, R., Goldstein, M. & White, R.G. (2015). Bayesian history matching of complex infectious disease models using emulation: A tutorial and a case study on HIV in Uganda. PLoS Computational Biology 11(1): e1003968.
  • Vernon, Ian, Goldstein, Michael & Bower, Richard (2014). Galaxy Formation: Bayesian History Matching for the Observable Universe. Statistical Science 29(1): 81-90.
  • Vernon, Ian., Goldstein, Michael. & Bower, Richard G. (2010). Galaxy Formation: a Bayesian Uncertainty Analysis. Bayesian Analysis 05(04): 619 - 670.
  • Vernon, Ian., Goldstein, Michael. & Bower, Richard G. (2010). Rejoinder - Galaxy Formation: a Bayesian Uncertainty Analysis. Bayesian Analysis 05(04): 697 - 708.
  • Bower, R. G., Vernon, I., Goldstein, M., Benson, A. J., Lacey, C. G., Baugh, C. M., Cole, S. & Frenk, C. S. (2010). The Parameter Space of Galaxy Formation. Monthly Notices of the Royal Astronomical Society 407(4): 2017-2045.
  • Jennings, David., Vernon, Ian R., Davis, Anne-Christine. & van de Bruck, Carsten. (2005). Bulk black holes radiating in non-Z_2 brane-world spacetimes. Journal of Cosmology and Astroparticle Physics 2005(04): 013.
  • Vernon, Ian R. & Jennings, David. (2005). Graviton Emission into Non-Z_2 Symmetric Brane World Spacetimes. Journal of Cosmology and Astroparticle Physics 07: 011.
  • Davis, Anne-Christine, Vernon, Ian, Davis, Stephen C. & Perkins, Warren B. (2001). Brane World Cosmology Without the Z_2 Symmetry. Physics Letters B 504(3): 254-261.
  • Davis, Anne-Christine. Rhodes, Christophe. & Vernon, Ian R. (2001). Branes on the Horizon. Journal of High Energy Physics 11: 015.
  • Davis, Stephen C., Perkins, Warren B., Davis, Anne-Christine & Vernon, Ian R. (2001). Cosmological phase transitions in a brane world. Physical Review D 63(8): 083518.

Conference Paper

  • Ferreira, C. J., Davolio, A., Schiozer, D. J., Vernon, I. & Goldstein, M. (2015), Use of Emulator and Canonical Correlation to Incorporate 4D Seismic Data in the Reduction of Uncertainty Process, EUROPEC 2015. Madrid, Spain, Society of Petroleum Engineers.
  • Ferreira, C., Vernon, I., Schiozer, D.J. & Goldstein, M. (2014), Use of Emulator Methodology for Uncertainty Reduction Quantification, SPE Latin America and Caribbean Petroleum Engineering Conference. Maracaibo, Venezuela, Society of Petroleum Engineers.
  • Vernon, Ian R. & Goldstein, M. (2009), Bayes Linear Analysis of Imprecision in Computer Models, with Application to Understanding Galaxy Formation, in Augustin, T. Coolen, F.P.A. Moral, S. & Troffaes, M.C.M. eds, 6: Sixth International Symposium on Imprecise Probability: Theories and Applications (ISIPTA'09). Durham University, SIPTA, 441-450.

Report

  • Vernon, Ian. R. & Goldstein, Michael (2010). A Bayes Linear Approach to Systems Biology. Sheffield, MUCM.
  • Vernon, Ian, Seheult, Allan & Goldstein, Michael (2010). Modular Dynamic Emulation and Internal Model Discrepancy for a Rainfall Runoff Model. Sheffield MUCM.
  • House, L., Goldstein, M. & Vernon, I. R. (2009). Exchangeable Computer Models. Sheffield, MUCM.
  • Vernon, Ian, Goldstein, Michael & Bower, Richard G. (2009). Galaxy Formation: a Bayesian Uncertainty Analysis. Sheffield MUCM.

Show all publications

Supervises