Staff profile

• Bayesian Statistics

• 2000: 'Committee Duties': Member of electoral college of EPSRC and Member of board of directors of the International Society for Bayesian Analysis

Authored book

• Bissell, J., Caiado, C., Curtis, S., Goldstein, M., & Straughan, B. (2015). Tipping Points: Modelling Social Problems and Health. Wiley. https://doi.org/10.1002/9781118992005
• Goldstein, M., & Wooff, D. (2007). Bayes Linear Statistics: Theory and Methods. John Wiley and Sons

Chapter in book

• Troffaes, M. C., & Goldstein, M. (2022). Foundations for temporal reasoning using lower previsions without a possibility space. In T. Augustin, F. Gagliardi Cozman, & G. Wheeler (Eds.), Reflections on the Foundations of Probability and Statistics: Essays in Honor of Teddy Seidenfeld (69-96). (1). Springer Verlag. https://doi.org/10.1007/978-3-031-15436-2_4
• Caiado, C., Hickey, G., Grant, S., Goldstein, M., Markarian, G., McCollum, C., & Bridgewater, B. (2015). Heart Online Uncertainty and Stability Estimation. In J. Bissell, C. Caiado, S. Curtis, M. Goldstein, & B. Straughan (Eds.), Tipping Points: Modelling Social Problems an Health. Wiley. https://doi.org/10.1002/9781118992005.ch5
• Goldstein, M. (2011). External Bayesian analysis for computer simulators. In J. Bernardo, M. Bayarri, J. Berger, A. Dawid, D. Heckerman, A. Smith, & M. West (Eds.), BAYESIAN STATISTICS 9. Oxford University Press
• Multiscale Computer Experiments. In A. O'Hagan, & M. West (Eds.), The Oxford Handbook of Applied Bayesian Analysis (241-270). Oxford University Press
• Coolen, F., Goldstein, M., & Wooff, D. (2005). Using Bayesian statistics to support testing of software systems. In J. Andrews (Ed.), Proceedings of the 16th Advances in Reliability Technology Symposium (109-121)
• Coolen, F., Goldstein, M., & Wooff, D. (2003). Project viability assessment for support of software testing via Bayesian graphical modelling. In T. Bedford, & P. van Gelder (Eds.), Safety and Reliability (417-422). Swets & Zeitlinger
• Goldstein, M., & Williams, D. (1999). Graphical diagnostics for the Bayes linear analysis of hierarchical linear models, with applications to educational data. In J. Bernardo, J. Berger, A. Dawid, & A. Smith (Eds.), Bayesian Statistics 6. Proceedings of the Sixth Valencia International Meeting (859-867). Oxford University Press
• Goldstein, M. (1999). Bayes linear analysis. In S. Kotz, C. Read, & D. Banks (Eds.), Encyclopaedia of Statistical Sciences Update Volume 3 (29-34). Wiley
• Goldstein, M., & Shaw, S. (1999). Simplifying complex designs : Bayes linear experimental design for grouped multivariate exchangeable systems. In J. Bernardo, J. Berger, A. Dawid, & A. Smith (Eds.), Bayesian Statistics 6. Proceedings of the Sixth Valencia International Meeting (839-848). Oxford University Press
• Goldstein, M., Farrow, M., & Spiropoulos, T. (1997). Developing a Bayes linear decision support system for a brewery. In S. French, & J. Smith (Eds.), The Practice of Bayesian Analysis (71-106). Edward Arnold
• Craig, P., Goldstein, M., Seheult, A., & Smith, J. (1997). Pressure matching for hydrocarbon reservoirs: a case in the use of Bayes linear strategies for large computer experiments (and discussion). In G. E. al (Ed.), Case studies in Bayesian Statistics (37-93). Springer Verlag
• Goldstein, M. (1997). Prior inferences for posterior judgements. In M. Chiara, K. Doets, D. Mundici, & J. Benthem (Eds.), Structure and norms in Science : Volume Two of the Tenth International Congress of Logic, Methodology and Philosophy of Science, Florence, August 1995 (55-71). Springer Netherlands. https://doi.org/10.1007/978-94-017-0538-7_4
• Goldstein, M., Craig, P., Seheult, A., & Smith, J. (1996). Bayes linear strategies for matching hydrocarbon reservoir history and discussion. In J. Bernardo, J. Berger, A. Dawid, & A. Smith (Eds.), Bayesian Statistics 4. Proceedings of the Fourth Valencia International Meeting: Dedicated to the memory of Morris H. DeGroot, 1931-1989: April 15-20, 1991 (69-95). Oxford University Press
• Goldstein, M. (1994). Belief revision : subjectivist principles and practice. In D. Prawitz, & D. Westerstahl (Eds.), Logic and Philosophy of Science in Uppsala (117-130). Springer Netherlands. https://doi.org/10.1007/978-94-015-8311-4_8
• Goldstein, M. (1994). Revising Exchangeable Beliefs: Subjectivist foundations for the inductive argument. In P. Freeman, & A. Smith (Eds.), Aspects of uncertainty: a tribute to D.V. Lindley (201-222). John Wiley and Sons
• Goldstein, M., & Farrow, M. (1992). Diagnostic geometry for Bayes linear prediction systems. In J. Bernardo, J. Berger, A. Dawid, & A. Smith (Eds.), Bayesian Statistics 4. Proceedings of the Fourth Valencia International Meeting: Dedicated to the memory of Morris H. DeGroot, 1931-1989: April 15-20, 1991 (561-568). Oxford University Press
• Goldstein, M., & Wilkinson, D. (1992). Bayes linear adjustment for variance matrices. In J. Bernardo, J. Berger, A. Dawid, & A. Smith (Eds.), Bayesian statistics 4. Proceedings of the Fourth Valencia International Meeting: Dedicated to the memory of Morris H. DeGroot, 1931-1989: April 15-20, 1991 (791-799). Oxford University Press

Conference Paper

• Formentin, H. N., Vernon, I., Goldstein, M., Caiado, C., Avansi, G., & Schiozer, D. (2020, September). Accounting for Model Discrepancy in Uncertainty Analysis by Combining Numerical Simulation and Bayesian Emulation Techniques. Presented at ECMOR XVII
• Ferreira, C., Avansi, G., Vernon, I., Schiozer, D., & Goldstein, M. (2018, September). Evaluation of Regions of Influence for Dimensionality Reduction in Emulation of Production Data. Presented at ECMOR XVI - 16th European Conference on the Mathematics of Oil Recovery, Barcenola, Spain
• Craig, P., Smith, J., Goldstein, M., & Seheult, A. (1995, December). Matching hydrocarbon reservoir history - a Bayes linear approach. Presented at Third International Applied Statistics in Industry Conference

Journal Article

• Williamson, D., & Goldstein, M. (online). Bayesian Policy Support for Adaptive Strategies using Computer Models for Complex Physical Systems. Journal of the Operational Research Society, https://doi.org/10.1057/jors.2011.110
• Williamson, D., & Goldstein, M. (online). Posterior Belief Assessment: Extracting Meaningful Subjective Judgements from Bayesian Analyses with Complex Statistical Models. Bayesian Analysis, 10(4), https://doi.org/10.1214/15-ba966si
• Domingo, D., Royapoor, M., Du, H., Boranian, A., Walker, S., & Goldstein, M. (2024). Calibration under Uncertainty Using Bayesian Emulation and History Matching: Methods and Illustration on a Building Energy Model. Energies, 17(16), Article 4014. https://doi.org/10.3390/en17164014
• Iskauskas, A., Vernon, I., Goldstein, M., Scarponi, D., McKinley, T. J., White, R. G., & McCreesh, N. (2024). Emulation and History Matching using the hmer Package. Journal of Statistical Software, 109(10), 1–48. https://doi.org/10.18637/jss.v109.i10
• Scarponi, D., Iskauskas, A., Clark, R. A., Vernon, I., McKinley, T. J., Goldstein, M., Mukandavire, C., Deol, A., Weerasuriya, C., Bakker, R., White, R. G., & McCreesh, N. (2023). Demonstrating multi-country calibration of a tuberculosis model using new history matching and emulation package - hmer. Epidemics, 43, Article 100678. https://doi.org/10.1016/j.epidem.2023.100678
• Dunne, M., Mohammadi, H., Challenor, P., Borgo, R., Porphyre, T., Vernon, I., Firat, E. E., Turkay, C., Torsney-Weir, T., Goldstein, M., Reeve, R., Fang, H., & Swallow, B. (2022). Complex model calibration through emulation, a worked example for a stochastic epidemic model. Epidemics, 39, Article 100574. https://doi.org/10.1016/j.epidem.2022.100574
• Swallow, B., Birrell, P., Blake, J., Burgman, M., Challenor, P., Coffeng, L. E., Dawid, P., De Angelis, D., Goldstein, M., Hemming, V., Marion, G., McKinley, T. J., Overton, C. E., Panovska-Griffiths, J., Pellis, L., Probert, W., Shea, K., Villela, D., & Vernon, I. (2022). Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling. Epidemics, 38, https://doi.org/10.1016/j.epidem.2022.100547
• Oughton, R., Goldstein, M., & Hemmings, J. (2022). Intermediate Variable Emulation: using internal processes in simulators to build more informative emulators. SIAM/ASA Journal on Uncertainty Quantification, 10(1), 268-293. https://doi.org/10.1137/20m1370902
• Wilson, A. L., Goldstein, M., & Dent, C. J. (2022). Varying Coefficient Models and Design Choice for Bayes Linear Emulation of Complex Computer Models with Limited Model Evaluations. SIAM/ASA Journal on Uncertainty Quantification, 10(1), 350-378. https://doi.org/10.1137/20m1318560
• Jones, M., Goldstein, M., Randell, D., & Jonathan, P. (2021). Bayes linear analysis for ordinary differential equations. Computational Statistics & Data Analysis, 161, Article 107228. https://doi.org/10.1016/j.csda.2021.107228
• Du, H., Sun, W., Goldstein, M., & Harrison, G. (2021). Optimization via Statistical Emulation and Uncertainty Quantification: Hosting Capacity Analysis of Distribution Networks. IEEE Access, 9, 118472-118483. https://doi.org/10.1109/access.2021.3105935
• Ferreira, C., Vernon, I., Caiado, C., Formentin, H., Avansi, G., Goldstein, M., & Schiozer, D. (2020). Efficient Selection of Reservoir Model Outputs within an Emulation-Based Bayesian History Matching Uncertainty Analysis. SPE Journal, 25(4), 2119-2142. https://doi.org/10.4043/29801-ms
• Formentin, H. N., Almeida, F. L. R., Avansi, G. D., Maschio, C., Schiozer, D. J., Caiado, C., Vernon, I., & Goldstein, M. (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. https://doi.org/10.1016/j.petrol.2018.10.045
• Jones, M., Goldstein, M., Jonathan, P., & Randell, D. (2018). Bayes linear analysis of risks in sequential optimal design problems. Electronic Journal of Statistics, 12(2), 4002-4031. https://doi.org/10.1214/18-ejs1496
• 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. https://doi.org/10.1016/j.petrol.2018.02.056
• Wilson, A., Dent, C., & Goldstein, M. (2018). Quantifying uncertainty in wholesale electricity price projections using Bayesian emulation of a generation investment model. Sustainable Energy, Grids and Networks, 13, 42-55. https://doi.org/10.1016/j.segan.2017.11.003
• Andrianakis, I., Vernon, I., McCreesh, N., McKinley, T., Oakley, J., Nsubuga, R., Goldstein, M., & White, R. (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, 66(4), 717-740. https://doi.org/10.1111/rssc.12198
• Caiado, C., & Goldstein, M. (2015). Bayesian uncertainty analysis for complex physical systems modelled by computer simulators with applications to tipping points. Communications in Nonlinear Science and Numerical Simulation, 26(1-3), 123-136. https://doi.org/10.1016/j.cnsns.2015.02.006
• Rougier, J., Goldstein, M., & House, L. (2013). "Second-order exchangeability analysis for multi-model ensembles. Journal of the American Statistical Association, 108(503), 852-863. https://doi.org/10.1080/01621459.2013.802963
• Williamson, D., Goldstein, M., & Blaker, A. (2012). Fast Linked Analyses for Scenario-based Hierarchies. Journal of the Royal Statistical Society: Series C, 61(5), 665-691. https://doi.org/10.1111/j.1467-9876.2012.01042.x
• Caiado, C. C., Goldstein, M., & Hobbs, R. W. (2012). Bayesian Strategies to Assess Uncertainty in Velocity Models. Bayesian Analysis, 7(1), 211-234. https://doi.org/10.1214/12-ba707
• Vernon, I., Goldstein, M., & Bower, R. G. (2010). Galaxy Formation: a Bayesian Uncertainty Analysis. Bayesian Analysis, 05(04), 619-670. https://doi.org/10.1214/10-ba524
• Randell, D., Goldstein, M., Hardman, G., & Jonathan, P. (2010). Bayesian linear inspection planning for large-scale physical systems. Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 224(4), 333-345. https://doi.org/10.1243/1748006xjrr322
• parameters using boundary linear utility. International Journal of Approximate Reasoning: Uncertainty in Intelligent Systems, 51, https://doi.org/10.1016/j.ijar.2010.08.002
• Cumming, J., & Goldstein, M. (2009). Small Sample Bayesian Designs for Complex High-Dimensional Models Based on Information Gained Using Fast Approximations. Technometrics, 51(4), 377-388. https://doi.org/10.1198/tech.2009.08015
• Goldstein, M., & Rougier, J. (2009). Reified Bayesian modelling and inference for physical systems. Journal of Statistical Planning and Inference, 139(3), 1221-1239. https://doi.org/10.1016/j.jspi.2008.07.019
• Farrow, M., & Goldstein, M. (2009). Almost-Pareto decision sets in imprecise utility hierarchies. Journal of statistical theory and practice, 3, 137-155
• Statistical Planning and Inference. Journal of Statistical Planning and Inference, 138, 1271-1286
• Goldstein, M., & Rougier, J. (2006). Bayes Linear Calibrated Prediction for Complex Systems. Journal of the American Statistical Association, 101(475), 1132-1143. https://doi.org/10.1198/016214506000000203
• Farrow, M., & Goldstein, M. (2006). Trade-off sensitive experimental design: a multicriterion, decision theoretic, Bayes linear approach. Journal of Statistical Planning and Inference, 136(2), 498-526. https://doi.org/10.1016/j.jspi.2004.07.008
• Goldstein, M. (2006). Subjective Bayesian analysis: principles and practice. Bayesian Analysis, 1, 403-420. https://doi.org/10.1214/06-ba116
• Goldstein, M., & Rougier, J. (2005). mathematical models to physical systems
• Goldstein, M., & Shaw, S. (2004). Bayes linear kinematics and Bayes linear Bayes Graphical Models. Biometrika, 91(2), 425-446. https://doi.org/10.1093/biomet/91.2.425
• Shaw, S., Goldstein, M., Munro, M., & Burd, E. (2003). Moral dominance relations for program comprehension. IEEE Transactions on Software Engineering, 29(9), 851-863. https://doi.org/10.1109/tse.2003.1232289
• Wooff, D., Goldstein, M., & Coolen, F. (2002). Bayesian Graphical Models for Software Testing. IEEE Transactions on Software Engineering, 28(5), 510-525. https://doi.org/10.1109/tse.2002.1000453
• Coolen, F., Goldstein, M., & Munro, M. (2001). Generalized partition testing via Bayes linear methods. Information and Software Technology, 43(13), 783-793. https://doi.org/10.1016/s0950-5849%2801%2900185-9
• Craig, P., Goldstein, M., Rougier, J., & Seheult, A. (2001). Bayesian forecasting for complex systems using computer simulators. Journal of the American Statistical Association, 96(454), 717-729. https://doi.org/10.1198/016214501753168370
• Goldstein, M. (2001). Avoiding foregone conclusions: geometric and foundational analysis of paradoxes of finite additivity. Journal of Statistical Planning and Inference, 94(1), 73-87. https://doi.org/10.1016/s0378-3758%2800%2900229-9
• Rougier, J., & Goldstein, M. (2001). A Bayesian Analysis of Fluid Flow in Pipelines. Journal of the Royal Statistical Society: Series C, 50(1), 77-93. https://doi.org/10.1111/1467-9876.00221
• Goldstein, M., & Wilkinson, D. (2001). Restricted prior inference for complex uncertainty structures. Annals of Mathematics and Artificial Intelligence, 32, 315-334. https://doi.org/10.1023/a%3A1016782020717
• Rees, K., Coolen, F., Goldstein, M., & Wooff, D. (2001). Managing the uncertainties of software testing: a Bayesian approach. Quality and Reliability Engineering International, 17, 191-203
• Wooff, D., & Goldstein, M. (2000). Bayes Linear Methods III - Analysing Bayes linear influence diagrams and Exchangeability in [B/D]. Journal of Statistical Software, 5(2),
• Goldstein, M., & Wilkinson, D. (2000). Bayes linear analysis for graphical models: the geometric approach to local computation and interpretive graphics. Statistics and Computing, 10, 311-324
• Goldstein, M., & Wooff, D. (1998). Adjusting exchangeable beliefs. Biometrika, 85, 39-54. https://doi.org/10.1093/biomet/85.1.39
• Craig, P., Goldstein, M., Seheult, A., & Smith, J. (1998). Constructing partial prior specifications for models of complex physical systems. Journal of the Royal Statistical Society. Series D, The statistician, 47(1), 37-53. https://doi.org/10.1111/1467-9884.00115
• Goldstein, M., & Wooff, D. (1997). Choosing samples sizes in balanced experimental designs: a Bayes linear approach. Statistician (London. Print), 46, 167-183
• Goldstein, M., & O'Hagan, A. (1996). Bayes linear sufficiency and systems of expert posterior assessments. Journal of the Royal Statistical Society: Series B, 58, 301-316
• Goldstein, M., & Wooff, D. (1995). Bayes linear computation: concepts, implementation and programming environment. Statistics and Computing, 5, 327-341
• Goldstein, M., & Wooff, D. (1994). Robustness measures for Bayes linear analysis (with discussion). Journal of Statistical Planning and Inference, 40, 261-277

Other (Digital/Visual Media)

• Wooff, D., & Goldstein, M. (1995). Beliefs adjusted by Data: the Bayes linear programming language, including software manuals and tutorials