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

• Tipping Points: Modelling Social Problems and Health
  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
• Bayes Linear Statistics: Theory and Methods
  Goldstein, M., & Wooff, D. (2007). Bayes Linear Statistics: Theory and Methods. John Wiley and Sons

Chapter in book

• Foundations for temporal reasoning using lower previsions without a possibility space
  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
• Heart Online Uncertainty and Stability Estimation
  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
• External Bayesian analysis for computer simulators
  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
• Bayes Linear Uncertainty Analysis for Oil Reservoirs Based on Multiscale Computer Experiments
  Multiscale Computer Experiments. In A. O'Hagan, & M. West (Eds.), The Oxford Handbook of Applied Bayesian Analysis (241-270). Oxford University Press
• Using Bayesian statistics to support testing of software systems.
  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)
• Project viability assessment for support of software testing via Bayesian graphical modelling
  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
• Bayes linear analysis.
  Goldstein, M. (1999). Bayes linear analysis. In S. Kotz, C. Read, & D. Banks (Eds.), Encyclopaedia of Statistical Sciences Update Volume 3 (29-34). Wiley
• Graphical diagnostics for the Bayes linear analysis of hierarchical linear models, with applications to educational data.
  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
• Simplifying complex designs : Bayes linear experimental design for grouped multivariate exchangeable systems.
  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
• Developing a Bayes linear decision support system for a brewery
  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
• Pressure matching for hydrocarbon reservoirs: a case in the use of Bayes linear strategies for large computer experiments (and discussion)
  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
• Prior inferences for posterior judgements.
  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
• Bayes linear strategies for matching hydrocarbon reservoir history and discussion.
  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
• Revising Exchangeable Beliefs: Subjectivist foundations for the inductive argument.
  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
• Belief revision : subjectivist principles and practice.
  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
• Diagnostic geometry for Bayes linear prediction systems.
  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
• Bayes linear adjustment for variance matrices.
  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

• Accounting for Model Discrepancy in Uncertainty Analysis by Combining Numerical Simulation and Bayesian Emulation Techniques
  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
• Evaluation of Regions of Influence for Dimensionality Reduction in Emulation of Production Data
  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
• Matching hydrocarbon reservoir history - a Bayes linear approach
  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

• Posterior Belief Assessment: Extracting Meaningful Subjective Judgements from Bayesian Analyses with Complex Statistical Models
  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
• Systematic structural discrepancy assessment for computer models
  Goldstein, M., Vernon, I., & Cumming, J. A. (2025). Systematic structural discrepancy assessment for computer models. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 383(2293), Article 20240214. https://doi.org/10.1098/rsta.2024.0214
• Calibration under Uncertainty Using Bayesian Emulation and History Matching: Methods and Illustration on a Building Energy Model
  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
• Emulation and History Matching using the hmer Package
  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
• Demonstrating multi-country calibration of a tuberculosis model using new history matching and emulation package - hmer
  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
• Complex model calibration through emulation, a worked example for a stochastic epidemic model
  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
• Challenges in estimation, uncertainty quantification and elicitation for pandemic modelling
  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
• Varying Coefficient Models and Design Choice for Bayes Linear Emulation of Complex Computer Models with Limited Model Evaluations
  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
• Intermediate Variable Emulation: using internal processes in simulators to build more informative emulators
  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
• Bayes linear analysis for ordinary differential equations
  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
• Optimization via Statistical Emulation and Uncertainty Quantification: Hosting Capacity Analysis of Distribution Networks
  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
• Efficient Selection of Reservoir Model Outputs within an Emulation-Based Bayesian History Matching Uncertainty Analysis
  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
• Gaining more understanding about reservoir behavior through assimilation of breakthrough time and productivity deviation in the history matching process
  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
• Bayes linear analysis of risks in sequential optimal design problems
  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
• Emulation of reservoir production forecast considering variation in petrophysical properties
  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
• Quantifying uncertainty in wholesale electricity price projections using Bayesian emulation of a generation investment model
  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
• History matching of a complex epidemiological model of human immunodeficiency virus transmission by using variance emulation
  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
• Bayesian uncertainty analysis for complex physical systems modelled by computer simulators with applications to tipping points
  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
• "Second-order exchangeability analysis for multi-model ensembles
  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
• Fast Linked Analyses for Scenario-based Hierarchies
  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
• Bayesian Strategies to Assess Uncertainty in Velocity Models
  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
• Bayesian Policy Support for Adaptive Strategies using Computer Models for Complex Physical Systems
  Williamson, D., & Goldstein, M. (2012). Bayesian Policy Support for Adaptive Strategies using Computer Models for Complex Physical Systems. Journal of the Operational Research Society, 63(8), 1021-1033 . https://doi.org/10.1057/jors.2011.110
• Galaxy Formation: a Bayesian Uncertainty Analysis
  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
• Sensitivity of decisions with imprecise utility trade-off parameters using boundary linear utility
  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
• Bayesian linear inspection planning for large-scale physical systems
  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
• Small Sample Bayesian Designs for Complex High-Dimensional Models Based on Information Gained Using Fast Approximations
  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
• Reified Bayesian modelling and inference for physical systems
  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
• Almost-Pareto decision sets in imprecise utility hierarchies
  Farrow, M., & Goldstein, M. (2009). Almost-Pareto decision sets in imprecise utility hierarchies. Journal of statistical theory and practice, 3, 137-155
• Prior viability assessment for Bayesian analysis, Journal of Statistical Planning and Inference
  Statistical Planning and Inference. Journal of Statistical Planning and Inference, 138, 1271-1286
• Bayes Linear Calibrated Prediction for Complex Systems
  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
• Trade-off sensitive experimental design: a multicriterion, decision theoretic, Bayes linear approach
  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
• Subjective Bayesian analysis: principles and practice.
  Goldstein, M. (2006). Subjective Bayesian analysis: principles and practice. Bayesian Analysis, 1, 403-420. https://doi.org/10.1214/06-ba116
• Probabilistic formulations for transferring inferences from mathematical models to physical systems.
  Goldstein, M., & Rougier, J. (2005). mathematical models to physical systems
• Bayes linear kinematics and Bayes linear Bayes Graphical Models
  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
• Moral dominance relations for program comprehension
  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
• Bayesian Graphical Models for Software Testing
  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
• Generalized partition testing via Bayes linear methods
  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
• Bayesian forecasting for complex systems using computer simulators
  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
• Avoiding foregone conclusions: geometric and foundational analysis of paradoxes of finite additivity
  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
• A Bayesian Analysis of Fluid Flow in Pipelines
  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
• Managing the uncertainties of software testing: a Bayesian approach.
  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
• Restricted prior inference for complex uncertainty structures
  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
• Bayes Linear Methods III - Analysing Bayes linear influence diagrams and Exchangeability in [B/D].
  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),
• Bayes linear analysis for graphical models: the geometric approach to local computation and interpretive graphics
  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
• Adjusting exchangeable beliefs.
  Goldstein, M., & Wooff, D. (1998). Adjusting exchangeable beliefs. Biometrika, 85, 39-54. https://doi.org/10.1093/biomet/85.1.39
• Constructing partial prior specifications for models of complex physical systems.
  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
• Choosing samples sizes in balanced experimental designs: a Bayes linear approach.
  Goldstein, M., & Wooff, D. (1997). Choosing samples sizes in balanced experimental designs: a Bayes linear approach. Statistician (London. Print), 46, 167-183
• Bayes linear sufficiency and systems of expert posterior assessments
  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
• Bayes linear computation: concepts, implementation and programming environment.
  Goldstein, M., & Wooff, D. (1995). Bayes linear computation: concepts, implementation and programming environment. Statistics and Computing, 5, 327-341
• Robustness measures for Bayes linear analysis (with discussion).
  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)

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