Staff profile

• Bayesian Statistics

• Probability & Statistics: Statistics
• Probability and Statistics

• Uncertainty analysis of hierarchical energy systems models: Models versus real energy systems

• 2020: Intensive Care Risk Identification System(£40000.00 from )
• 2016: HubNet(£9852.75 from Epsrc)
• 2016: The Intensive care risk identification system(£122850.00 from )
• 2015: Predictor Feasibility Study(£23810.00 from Innovate UK)
• 2012: RF060151: Calibration and analysis of complex individual-based stochastic models: methodological development and application to explore the impact of HAART on HIV/AIDS in Africa, MRC, £39529.24, 2012-10-01 - 2016-03-31
• 2008: JOINT INVERSION WITH BAYESIAN ANALYSIS(£182333.26 from Industry Technology Facilitator)

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

Authored book

• Goldstein, M. & Wooff, D. A. (2007). Bayes Linear Statistics: Theory and Methods. Chichester: John Wiley.

Chapter in book

• Goldstein, M. (2011). External Bayesian analysis for computer simulators. In BAYESIAN STATISTICS 9. Bernardo, J.M., Bayarri, M.J., Berger, J.O., Dawid, A.P., Heckerman, D., Smith, A.F.M. & West, M. Oxford University Press.
• Cumming, J. & Goldstein, M. (2009). Bayes Linear Uncertainty Analysis for Oil Reservoirs Based on Multiscale Computer Experiments. In The Oxford Handbook of Applied Bayesian Analysis. O'Hagan, & West,A.M. Oxford University Press. 241-270.
• Coolen, F. P., Goldstein, M. & Wooff, D. A. (2005). Using Bayesian statistics to support testing of software systems. In Proceedings of the 16th Advances in Reliability Technology Symposium. Andrews, J. 109-121.
• Coolen, F. P. A., Goldstein, M. & Wooff, D. A. (2003). Project viability assessment for support of software testing via Bayesian graphical modelling. In Safety and Reliability. Bedford & van Gelder Lisse: Swets & Zeitlinger. 417-422.
• Goldstein, M. & Shaw, S.C. (1999). Simplifying complex designs Bayes linear experimental design for grouped multivariate exchangeable systems. In Bayesian Statistics 6. Proceedings of the Sixth Valencia International Meeting. Bernardo, J.M., Berger, J.O., Dawid, A.P. & Smith, A.F.M. Oxford: Oxford University Press. 839-848.
• Goldstein, M. (1999). Bayes linear analysis. In Encyclopaedia of Statistical Sciences Update Volume 3. Kotz, S., Read, C.B. & Banks, D.L. New York: Wiley. 29-34.
• Goldstein, M. & Williams, D.R. (1999). Graphical diagnostics for the Bayes linear analysis of hierarchical linear models, with applications to educational data. In Bayesian Statistics 6. Proceedings of the Sixth Valencia International Meeting. Bernardo, J.M., Berger, J.O., Dawid, A.P. & Smith, A.F.M. Oxford: Oxford University Press. 859-867.
• Goldstein, M. (1997). Prior inferences for posterior judgements. In Structure and norms in Science Volume Two of the Tenth International Congress of Logic, Methodology and Philosophy of Science, Florence, August 1995. Chiara, M.L.D., Doets, K., Mundici, D. & Benthem, J.V. Springer Netherlands. 55-71.
• Goldstein, M., Farrow, M. & Spiropoulos, T. (1997). Developing a Bayes linear decision support system for a brewery. In The Practice of Bayesian Analysis. French, S. & Smith, J.Q. Edward Arnold. 71-106.
• Craig, P.S., Goldstein, M., Seheult, A.H. & Smith, J.A. (1997). Pressure matching for hydrocarbon reservoirs: a case in the use of Bayes linear strategies for large computer experiments (and discussion). In Case studies in Bayesian Statistics. Gatsonis et al New York: Springer-Verlag. III: 37-93.
• Goldstein, M., Craig, P.S., Seheult, A.H. & Smith, J.A. (1996). Bayes linear strategies for matching hydrocarbon reservoir history and discussion. In Bayesian Statistics 4. Proceedings of the Fourth Valencia International Meeting: Dedicated to the memory of Morris H. DeGroot, 1931-1989: April 15-20, 1991. Bernardo, J.M., Berger, J.O., Dawid, A.P. & Smith, A.F.M. Oxford: Oxford University Press. 69-95.
• Goldstein, M. (1994). Belief revision subjectivist principles and practice. In Logic and Philosophy of Science in Uppsala. Prawitz, D. & Westerstahl, D. Springer Netherlands. 117-130.
• Goldstein, M. (1994). Revising Exchangeable Beliefs: Subjectivist foundations for the inductive argument. In Aspects of uncertainty: a tribute to D.V. Lindley. Freeman, P.R. & Smith, A.F.M. Chichester: John Wiley. 201-222.
• Goldstein, M. & Wilkinson, D.J. (1992). Bayes linear adjustment for variance matrices. In Bayesian statistics 4. Proceedings of the Fourth Valencia International Meeting: Dedicated to the memory of Morris H. DeGroot, 1931-1989: April 15-20, 1991. Bernardo, J.M., Berger, J.O., Dawid, A.P. & Smith, A.F.M. Oxford: Oxford University Press. 791-799.
• Goldstein, M. & Farrow, M. (1992). Diagnostic geometry for Bayes linear prediction systems. In Bayesian Statistics 4. Proceedings of the Fourth Valencia International Meeting: Dedicated to the memory of Morris H. DeGroot, 1931-1989: April 15-20, 1991. Bernardo, J.M., Berger, J.O., Dawid, A.P. & Smith, A.F.M. Oxford: Oxford University Press. 561-568.

Conference Proceeding

• Craig, P.S., Smith, J.A., Goldstein, M. & Seheult, A.H. (1995). Matching hydrocarbon reservoir history - a Bayes linear approach. Third International Applied Statistics in Industry Conference.

Journal Article

• Wilson, Amy L., Goldstein, Michael & Dent, Chris 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.
• Jones, Matthew, Goldstein, Michael, Randell, David & Jonathan, Philip (2021). Bayes linear analysis for ordinary differential equations. Computational Statistics & Data Analysis 161: 107228.
• Jones, Matthew, Goldstein, Michael, Jonathan, Philip & Randell, David (2018). Bayes linear analysis of risks in sequential optimal design problems. Electronic Journal of Statistics 12(2): 4002-4031.
• Wilson, A.L., Dent, C.J. & 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.
• Williamson, Daniel & Goldstein, Michael (2015). Posterior Belief Assessment: Extracting Meaningful Subjective Judgements from Bayesian Analyses with Complex Statistical Models. Bayesian Analysis 10(4): 877.
• 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.
• Williamson, D., Goldstein, M. & Blaker, A. (2012). Fast Linked Analyses for Scenario-based Hierarchies. Journal of the Royal Statistical Society, Series C: Applied Statistics 61(5): 665-691.
• Williamson, D & Goldstein, M (2011). Bayesian Policy Support for Adaptive Strategies using Computer Models for Complex Physical Systems. Journal of the Operational Research Society
• Vernon, Ian., Goldstein, Michael. & Bower, Richard G. (2010). Galaxy Formation: a Bayesian Uncertainty Analysis. Bayesian Analysis 05(04): 619 - 670.
• 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.
• Farrow, M. & Goldstein, M. (2010). Sensitivity of decisions with imprecise utility trade-off parameters using boundary linear utility. International Journal of Approximate Reasoning 51: 1100–1113.
• Cumming, J. A. & Goldstein, M. (2009). Small Sample Bayesian Designs for Complex High-Dimensional Models Based on Information Gained Using Fast Approximations. Technometrics 51(4): 377-388.
• Farrow, M. & Goldstein, M. (2009). Almost-Pareto decision sets in imprecise utility hierarchies. Journal of Statistical Theory and Practice 3: 137-155.
• Goldstein, M. & Rougier, J.C. (2009). Reified Bayesian modelling and inference for physical systems. Journal of Statistical Planning and Inference 139(3): 1221-1239.
• Goldstein, M. & Seheult, A.H. (2008). Prior viability assessment for Bayesian analysis, Journal of Statistical Planning and Inference. Journal of Statistical Planning and Inference 138: 1271-1286
• Goldstein, M. & Rougier, J.C. (2006). Bayes Linear Calibrated Prediction for Complex Systems. Journal of the American Statistical Association 101(475): 1132-1143.
• 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.
• Goldstein, M. (2006). Subjective Bayesian analysis: principles and practice. Bayesian Analysis 1: 403-420.
• Goldstein, M. & Rougier, J.C. (2005). Probabilistic formulations for transferring inferences from mathematical models to physical systems. SIAM journal on scientific computing. SIAM journal on scientific computing 26: 467-487.
• Goldstein, M. & Shaw S. (2004). Bayes linear kinematics and Bayes linear Bayes Graphical Models. Biometrika 91(2): 425-446.
• Shaw, S.C., Goldstein, M., Munro, M. & Burd, E. (2003). Moral dominance relations for program comprehension. IEEE Transactions on Software Engineering 29(9): 851-863.
• Wooff, D. A., Goldstein, M. & Coolen, F. P. A. (2002). Bayesian Graphical Models for Software Testing. IEEE Transactions on Software Engineering 28(5): 510-525.
• Coolen, F. P. A., Goldstein M. & Munro, M. (2001). Generalized partition testing via Bayes linear methods. Information and software technology 43(13): 783-793.
• Craig, P. S., Goldstein, M., Rougier, J. C. & Seheult, A. H. (2001). Bayesian forecasting for complex systems using computer simulators. Journal of the American Statistical Association 96(454): 717-729.
• Goldstein, M. & Wilkinson, D.J. (2001). Restricted prior inference for complex uncertainty structures. Annals of Mathematics and Artificial Intelligence 32: 315-334.
• Rougier, J.C. & Goldstein, M. (2001). A Bayesian Analysis of Fluid Flow in Pipelines. Applied statistics a journal of the Royal Statistical Society 50(1): 77-93.
• Goldstein, Michael (2001). Avoiding foregone conclusions: geometric and foundational analysis of paradoxes of finite additivity. Journal of Statistical Planning and Inference 94(1): 73-87.
• Rees, K., Coolen, F. P. A., Goldstein, M. & Wooff, D. A. (2001). Managing the uncertainties of software testing: a Bayesian approach. Quality and Reliability Engineering International 17: 191-203.
• Goldstein, M. & Wilkinson, D.J. (2000). Bayes linear analysis for graphical models: the geometric approach to local computation and interpretive graphics. Statistics and Computing 10: 311-324.
• Wooff, D. A. & 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. & Wooff, D. A. (1998). Adjusting exchangeable beliefs. Biometrika 85: 39-54.
• Craig, P., Goldstein, M., Seheult, A. H. & Smith, J. A. (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.
• Goldstein, M. & Wooff, D. A. (1997). Choosing samples sizes in balanced experimental designs: a Bayes linear approach. Journal of the Royal Statistical Society: Series D 46: 167-183.
• Goldstein M & A. O'Hagan (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. A. (1995). Bayes linear computation: concepts, implementation and programming environment. Statistics and Computing 5: 327-341.
• Goldstein, M. & Wooff, D. A. (1994). Robustness measures for Bayes linear analysis (with discussion). Journal of Statistical Planning and Inference 40: 261-277.

Other (Digital/Visual Media)

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