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Durham University

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Staff Profile

Ian Vernon, PhD University of Cambridge

ORCID profile
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

  • Ferreira, Carla, Vernon, Ian, Caiado, Camila, Formentin, Helena, Avansi, Guilherme, Goldstein, Michael & Schiozer, Denis (2019). Efficient Selection of Reservoir Model Outputs within an Emulation-Based Bayesian History Matching Uncertainty Analysis. SPE Journal OTC-29801-MS, 1-34.
  • Formentin, Helena Nandi, Almeida, Forlan la Rosa, Avansi, Guilherme Daniel, Maschio, Célio, Schiozer, Denis J., Caiado, Camila, Vernon, Ian & Goldstein, Michael (2019). Gaining more understanding about reservoir behavior through assimilation of breakthrough time and productivity deviation in the history matching process. Journal of Petroleum Science and Engineering 173: 1080-1096.
  • Vernon, I. R., Jackson, S. E. & Cumming, J. A. (2019). Known Boundary Emulation of Complex Computer Models. SIAM/ASA Journal on Uncertainty Quantification 7(3): 838-876.
  • 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, I., Liu, J., Goldstein, M., Rowe, J., Topping, J. & Lindsey, K. (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.
  • 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.
  • 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.

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Supervises