Reading List on Bayes Linear suggested by Michael Goldstein

Goldstein (1998) Bayes Linear Analysis. Encyclopaedia of Statistical Sciences. Wiley. This is something I've used in the past to introduce the ideas to people - but for the purposes of the working group it might be both too broad in covering too many topics, while not deep enough in what it does cover.

P.S. Craig, M. Goldstein, A.H. Seheult, J.A. Smith (1996). Bayes linear strategies for matching hydrocarbon reservoir history, (with discussion) in Bayesian Statistics 5, eds. J.M. Bernardo, et al., Oxford University Press, 69-95.

There is far more detail on what we actually do in P.S. Craig, M. Goldstein, A.H. Seheult, J.A. Smith (1997). Pressure matching for hydocarbon reservoirs: a case study in the use
of Bayes linear strategies for large computer experiments (with discussion) in Case Studies in Bayesian Statistics, vol. III, eds. C. Gastonis et al. 37-93. Springer-Verlag.

These papers are both about `history matching' or calibration. Our forecasting approach is in:

P. S. Craig, M. Goldstein, J. C. Rougier and A. H. Seheult (2001) Bayesian Forecasting Using Large Computer Models, JASA 96, 717-729 (notice that we manage to forecast without calibrating first, which makes everything much more tractable ).

Two current papers are:

M. Goldstein and J.Rougier (2006), Bayes Linear Calibrated Prediction for Complex Systems, Journal of the American Statistical Association/, forthcoming. This paper was previously entitled "Calibrated Bayesian forecasting using large computer simulators". It mixes calibration with prediction while retaining tractability even for very high dimensions - still no MCMC!

M. Goldstein and J.Rougier (2006), Reified Bayesian Modelling and Inference for Physical Systems. Journal of Statistical Planning and Inference/, forthcoming as a discussion paper, subject to final approval. This is our `linking models with reality paper' - with example.

and one more:

P.S. Craig, M. Goldstein, A.H. Seheult, J.A. Smith (1998), Constructing partial prior specifications for models of complex physical systems. The Statistician, 47, 37-53, which discusses belief specification for these models.