Jonathan (Jonty) Rougier's homepage
Current roles:
Research/ActivitiesI do science in areas of high uncertainty. Here are my main research/activity areas.
Some current projects
Exciting book!Publications
Recent (since 2018)J.C. Rougier (2019). Confidence in risk assessments. Journal of the Royal Statistical Society, Ser. A, forthcoming. J.C. Rougier (2019). Pvalues, Bayes factors, and sufficiency, The American Statistician, 73, supp1. doi:10.1080/00031305.2018.1502684. Part of the special issue Statistical Inference in the 21st Century: A World Beyond p < 0.05. Z. Sha, J.C. Rougier, M. Schumacher, and J.L. Bamber (2019). Bayesian modeldata synthesis with an application to global GlacioIsostatic Adjustment, Environmetrics, 30(1), e2530. doi:10.1002/env.2530 L. Hawker, P. Bates, J. Neal, J.C. Rougier (2018). Perspectives on Digital Elevation Model (DEM) simulation for flood modelling in the absence of a highaccuracy open access global DEM. Frontiers in Earth Science, 6. doi:10.3389/feart.2018.00233 A. Zammit Mangion and J.C. Rougier (2018). A sparse linear algebra algorithm for fast computation of prediction variances with Gaussian Markov random fields, Computational Statistics and Data Analysis, 123, 116130. doi:10.1016/j.csda.2018.02.001 L. Hawker, J.C. Rougier, J. Neal, P. Bates, L. Archer, and D. Yamazaki (2018). Implications of simulating global digital elevation models for flood inundation studies, Water Resources Research, 54(10), 79107928. doi:10.1029/2018WR023279 J.C. Rougier (2018). Comment on "Ensemble averaging and the curse of dimensionality", Journal of Climate, 31, 90159016. doi:10.1175/JCLID180274.1. Author's response: 10.1175/JCLID180416.1. M. Schumacher, M. King, J.C. Rougier, Z. Sha, S.A. Khan, and J. Bamber (2018), A new global GPS dataset for testing and improving modelled GIA uplift rates, Geophysical Journal International, 214(3), 21642176. doi:10.1093/gji/ggy235 L.M. Western, J.C. Rougier, and I.M. Watson (2018), Decision theorybased detection of atmospheric natural hazards from satellite imagery using the example of volcanic ash. Quarterly Journal of the Royal Meteorological Society, 144, 581587. doi:10.1002/qj.3230 J.C. Rougier, R.S.J. Sparks, and K.V. Cashman (2018), Regional and global underrecording of large explosive eruptions in the last 1000 years, Journal of Applied Volcanology, 7:1, doi:10.1186/s1361701700709 J.C. Rougier, R.S.J. Sparks, K.V. Cashman, and S.K. Brown (2018), The global magnitudefrequency relationship for large explosive volcanic eruptions, Earth and Planetary Science Letters, 482, 621629. doi:10.1016/j.epsl.2017.11.015. Read the press release. Summary in LMS newsletter, pp2731. Older Statistics and ProbabilityI. Gollini and J.C. Rougier (2016). Rapidly bounding the exceedance probabilities of high aggregate losses. Journal of Operational Risk, 11(3), 97116. doi:10.21314/JOP.2016.179 J.C. Rougier and A. Zammit Mangion (2016). Visualisation for largescale Gaussian updates. Scandinavian Journal of Statistics, 43(4), 11531161. doi:10.1111/sjos.12234 A. Zammit Mangion, J.C. Rougier, N.W. Schoen, F. Lindgren, J.L. Bamber (2015). Multivariate spatiotemporal modelling for assessing Antarctica's presentday contribution to sealevel rise. Environmetrics, 26(3), 159177. doi:10.1002/env.2323 J.C. Rougier and M. Goldstein (2014), Climate Simulators and Climate Projections, Annual Review of Statistics and Its Application, 1, 103123. doi:10.1146/annurevstatistics022513115652 A. Zammit Mangion, J.C. Rougier, J.L. Bamber and N.W. Schoen (2014), Resolving the Antarctic contribution to sealevel rise: a hierarchical modelling framework, Environmetrics, 25(4), 245264. doi:10.1002/env.2247 J.C. Rougier, M. Goldstein, and L. House (2013), Secondorder exchangeability analysis for multimodel ensembles, Journal of the American Statistical Association, 108, 852863. doi:10.1080/01621459.2013.802963 I. Scheel, P.J. Green, and J.C. Rougier (2011), A graphical diagnostic for identifying influential model choices in Bayesian hierarchical models, Scandinavian Journal of Statistics, 38(3), 529550. doi:10.1111/j.14679469.2010.00717.x J.C. Rougier (2010), Discussion of "A statistical analysis of multiple temperature proxies: Are reconstructions of surface temperatures over the last 1000 years reliable?" by McShane and Wyner, Annals of Applied Statistics, 5(1), 9698. doi:10.1214/10AOAS409 J.C. Rougier and M. Kern (2010), Predicting Snow Velocity in Large Chute Flows Under Different Environmental Conditions. Applied Statistics, 59(5), 737760. doi:10.1111/j.14679876.2010.00717.x J.C. Rougier, S. Guillas, A. Maute, A.D. Richmond (2009), Expert Knowledge and Multivariate Emulation: The ThermosphereIonosphere Electrodynamics General Circulation Model (TIEGCM), Technometrics, 51(4), 414424. doi:10.1198/TECH.2009.07123 M. Goldstein and J.C. Rougier (2009), Reified Bayesian Modelling and Inference for Physical Systems, Journal of Statistical Planning and Inference, 139(3), 12211239. doi:10.1016/j.jspi.2008.07.019 With discussion and rejoinder. J.C. Rougier (2008), Efficient Emulators for Multivariate Deterministic Functions, Journal of Computational and Graphical Statistics, 17(4), 827843. doi:10.1198/106186008X384032. R package OPE_0.8.tar.gz. J.C. Rougier (2008), Discussion of 'Inferring Climate System Properties Using a Computer Model', by Sanso et al, Bayesian Analysis, 3(1), 4556. doi:10.1214/08BA301B M. Goldstein and J.C. Rougier (2006), Bayes Linear Calibrated Prediction for Complex Systems, Journal of the American Statistical Association, 101 (no. 475), 11321143. M. Goldstein and J.C. Rougier (2004), Probabilistic Formulations for Transferring Inferences from Mathematical Models to Physical Systems, SIAM Journal on Scientific Computing, 26(2), 467487. doi:/10.1137/S106482750342670X P.S. Craig, M. Goldstein, J.C. Rougier and A.H. Seheult (2001), Bayesian Forecasting for Complex Systems Using Computer Simulators, Journal of the American Statistical Association, 96, 717729. Older other scienceT. Economou, D. Stephenson, J.C. Rougier, K. Mylne, and R. Neal (2016), On the use of Bayesian decision theory for issuing natural hazard warnings, Proceedings of the Royal Society, Series A, 472, 20160295, 19 pages. doi:10.1098/rspa.2016.0295 J.C. Rougier (2016), Ensemble averaging and mean squared error. Journal of Climate, 29, 88658870. doi:10.1175/JCLID160012.1 J.C. Rougier, S. Sparks, and K. Cashman (2016), Global recording rates for large eruptions, Journal of Applied Volcanology, 5:11, 10 pages. doi:10.1186/s1361701600514 F. Pianosi, K. Beven. J. Freer, J.W. Hall, J.C. Rougier, D.B. Stephenson, and T. Wagener (2016), Sensitivity analysis of environmental models: A systematic review with practical workflow, Environmental Modelling and Software, 79, 214232. doi:10.1016/j.envsoft.2016.02.008 M. Woodhouse, A. Hogg, J. Phillips, and J.C. Rougier (2015), Uncertainty analysis of a model of windblown volcanic plumes, Bulletin of Volcanology, October 77:83, 28 pages, doi:10.1007/s0044501509592 N. Schoen, A. ZammitMangion, J.C. Rougier, T. Flament, F. Rémy, S. Luthcke, and J.L. Bamber (2015), Simultaneous solution for mass trends on the West Antarctic Ice Sheet, The Cryosphere, 9, 805819. doi:10.5194/tc98052015 A. Zammit Mangion, J.L. Bamber, N. Schoen, and J.C. Rougier (2015), A datadriven approach for assessing icesheet mass balance in space and time, Annals of Glaciology, 56, 175183. doi:10.3189/2015AoG70A021 J.C. Rougier (2013), 'Intractable and unsolved': some thoughts on statistical data assimilation with uncertain static parameters, Phil. Trans R. Soc. A., 371, doi:10.1098/rsta.2012.0297 D.B. Stephenson, M. Collins, J.C. Rougier, and R.E. Chandler (2012), Statistical problems in the probabilistic prediction of climate change, Environmetrics, 23(5), 364372. doi: 10.1002/env.2153 M. Collins, R.E. Chandler, P.M. Cox, J.M. Huthnance, J.C. Rougier, and D.B. Stephenson (2012), Quantifying future climate change, Nature Climate Change, 2, 403409. doi:10.1038/nclimate1414 R.M. Gladstone, V. Lee, J.C. Rougier, A.J. Payne, H. Hellmer, A. Le Brocq, A. Shepherd, T.L. Edwards, J. Gregory, and S.L. Cornford (2012), Calibrated prediction of Pine Island Glacier retreat during the 21st and 22nd centuries with a coupled flowline model, Earth and Planetary Science Letters, 333334, 191199. doi:10.1016/j.epsl.2012.04.022 P.W. Fitzgerald, J.L. Bamber, J. Ridley and J.C. Rougier (2011), Exploration of parametric uncertainty in a Surface Mass Balance Model applied to the Greenland Ice Sheet, Journal of Geophysical Research, 117, F01021, doi:10.1029/2011JF002067 N.R. Edwards, D. Cameron, J.C. Rougier (2011), Precalibrating an intermediate complexity climate model, Climate Dynamics, 37, 14691482. S. Guillas, J.C. Rougier, A. Maute, A.D. Richmond, and C.D. Linkletter (2009), Bayesian calibration of the ThermosphereIonosphere Electrodynamics General Circulation Model (TIEGCM), Geoscientific Model Development, 2, 137144. Available online. M. Crucifix and J.C. Rougier (2009), On the use of simple dynamical systems for climate predictions: A Bayesian prediction of the next glacial inception. The European Physics Journal  Special Topics, 174(1), 1131. doi:10.1140/epjst/e2009010875 J.C. Rougier, D.M.H. Sexton, J.M. Murphy, and D. Stainforth (2009), Analysing the climate sensitivity of the HadSM3 climate model using ensembles from different but related experiments. Journal of Climate, 22(13), 35403557. doi:10.1175/2008JCLI2533.1 J.C. Rougier and D.M.H. Sexton (2007), Inference in Ensemble Experiments, Philosophical Transactions of the Royal Society, Series A, 365, 21332143. doi:10.1098/rsta.2007.2071 J.C. Rougier (2007), Probabilistic Inference for Future Climate Using an Ensemble of Climate Model Evaluations, Climatic Change, 81, 247264. doi:10.1007/s1058400691569 J.C Rougier (2005), Probabilistic Leak Detection in Pipelines Using the Mass Imbalance Approach. Journal of Hydraulic Research, 43(5), 556566. M. van Oijen, J.C. Rougier and R. Smith (2005), Bayesian Calibration of ProcessBased Forest Models: Bridging the Gap Between Models and Data, Tree Physiology, 25, 915927. Economics and FinanceS.C. Parker and J.C. Rougier (2007), The Retirement Behaviour of the SelfEmployed in Britain, Applied Economics, 39(6), 697713. P.R. Holmes and J.C. Rougier (2005), Trading Volume and Contract Rollover in Futures Contracts, Journal of Empirical Finance, 12(2), 317338. S.C. Parker and J.C. Rougier (2001), Measuring Social Mobility as Unpredictability, Economica, 68, 6376. B. Hillier and J.C. Rougier (1999), Real Business Cycles, Investment Finance and Multiple Equilibria, Journal of Economic Theory, 86, 10022. J.C. Rougier (1997), A Simple Necessary Condition for Negativity in the Almost Ideal Demand System with the Stone Price Index, Applied Economics Letters, 4, 979. J.C. Rougier (1996), An Optimal Price Index for Stock Index Futures Contracts, Journal of Futures Markets, 16, 18999. J.C. Rougier (1993), The Impact of MarginTraders on the Distribution of Daily Stock Returns: The London Stock Exchange, Applied Financial Economics, 3, 3258. Books and book chaptersJ.C. Rougier and M. Crucifix (2017), Uncertainty in climate science and climate policy, in L. Lloyd and E. Winsberg, eds, Climate Modeling: Philosophical and Conceptual Issues, Palgrave Macmillan, chapter 12, pages 361380. J.C Rougier (2014), Formal Bayes methods for model calibration with uncertainty, in K. Beven and J. Hall (eds), Applied Uncertainty Analysis for Flood Risk Management, Imperial College Press, chapter 5, pages 6886. J.C. Rougier, R.S.J. Sparks, and L.J. Hill (eds), 2013, Risk and Uncertainty Assessment for Natural Hazards, Cambridge, UK: Cambridge University Press.
NonpeerreviewedK. Milner and J.C. Rougier (2014), "How to weigh a donkey in the Kenyan countryside", Significance, 11(4), 4043. doi:10.1111/j.17409713.2014.00768.x J. Murphy, R. Clark, M. Collins, C. Jackson, M. Rodwell, J.C. Rougier, B. Sanderson, D. Sexton and T. Yokohata (2011), Perturbed parameter ensembles as a tool for sampling model uncertainties and making climate projections, Proceedings of ECMWF Workshop on Model Uncertainty, 2024 June 2011, 183208. Available online. J.C. Rougier, T.L. Edwards, M. Collins and D.M.H. Sexton (2011), Lownoise projections of complex simulator output: A useful tool when checking for code errors, Proceedings of ECMWF Workshop on Model Uncertainty, 2024 June 2011, 209220. Available online. J.C. Rougier and L. Chen (2010), Comment on the paper by Diggle et al., Journal of the Royal Statistical Society, Series C, 59(2), 216. R. Chandler, J.C. Rougier, and M. Collins (2010), Climate change, Significance, 7(1), 912. J.C. Rougier (2009), Notes on statistical modelling for complex systems, ver. 0.5, unpublished. Available as a pdf file. Please note the version number: this document is still evolving. J.C. Rougier (2008), Climate change detection and attribution, ISBA bulletin, 15(4), 36. Available online. J.C. Rougier (2006), Comment on the paper by Haslett et al., Journal of the Royal Statistical Society, Series A, 169(3), 432433. J.C. Rougier (2005), Literate Programming for Creating and Maintaining Packages. R News, 5(1), 3539. J.C. Rougier (2004), Comment on the paper by Murphy et al. Nature did not want to publish this comment, but I think it says some useful things. Available as a pdf file. J.C. Rougier (2001), Comment on the paper by Kennedy and O'Hagan, Journal of the Royal Statistical Society, Series B, 63, page 453. J.C. Rougier (2001), What's the Point of `tensor'?, R News, 1(2), 2627. MiscellaneousSelected presentations (since 2009)
SoftwareR packages
Other resources
Donkeys!Joint work with Kate Milner.Donkeys are extremely hard to weigh, but easy to measure with a measuring tape. Because weight is a important factor in health and in veterinary care, statistical models are used to predict weight on the basis of measured hearth girth and height. The Donkey Sanctuary sponsored Kate Milner to go to Kenya with a large weighing scales and collect data on donkeys. Together we are analysing the results, with the intention of producing an improved 'nomogram' (or similar) showing how to predict a Kenyan donkey's weight on the basis of its height and heart girth, and also age and condition. Resources:
Setting Up Your SimulatorSetting Up Your Simulator (SUYS) is my procedure for the early stages of developing a computer simulator for a complicated system. Here is a the Abstract from the current draft."The parameters of a computer simulator are often poorly defined, and their ranges for a particular application can be mysterious. But the negative consequences of getting these ranges wrong, either too small or too large, endure through all subsequent uses. So 'settingup', by which I mean the process of adjusting the individual ranges in the light of a few carefullychosen observations, is a crucial first step. Happily there is a relatively straightforward process for settingup, based on the notion of 'implausibility', and making use of simple calculations and visualisations. This process works much better if the analyst is able to proceed sequentially, through several waves of runs. The paper also considers the extension of this process to different types of simulator: simulators with large fields of parameters, stochastic simulators, and expensivetorun simulators."
