Outputs (6)

Probabilistic Inference for Future Climate Using an Ensemble of Climate Model Evaluations (2007)
Journal Article
Rougier, J. (2007). Probabilistic Inference for Future Climate Using an Ensemble of Climate Model Evaluations. Climatic Change, 81(3-4), 247-264. https://doi.org/10.1007/s10584-006-9156-9

This paper describes an approach to computing probabilistic assessments of future climate, using a climate model. It clarifies the nature of probability in this context, and illustrates the kinds of judgements that must be made in order for such a pr... Read More about Probabilistic Inference for Future Climate Using an Ensemble of Climate Model Evaluations.

Bayes Linear Calibrated Prediction for Complex Systems (2006)
Journal Article
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

A calibration-based approach is developed for predicting the behavior of a physical system that is modeled by a computer simulator. The approach is based on Bayes linear adjustment using both system observations and evaluations of the simulator at pa... Read More about Bayes Linear Calibrated Prediction for Complex Systems.

Trading volume and contract rollover in futures contracts (2005)
Journal Article
Holmes, P., & Rougier, J. (2005). Trading volume and contract rollover in futures contracts. Journal of Empirical Finance, 12(2), 317-338. https://doi.org/10.1016/j.jempfin.2004.01.003

Futures trading volume data display strong quarterly seasonality due to the ‘rolling over’ of positions close to the expiry date of the near contract. This undermines the use of volume as a proxy for information arrival. By making explicit the relati... Read More about Trading volume and contract rollover in futures contracts.

Probabilistic Formulations for Transferring Inferences from Mathematical Models to Physical Systems (2004)
Journal Article
Goldstein, M., & Rougier, J. (2004). Probabilistic Formulations for Transferring Inferences from Mathematical Models to Physical Systems. SIAM Journal on Scientific Computing, 26(2), 467-487. https://doi.org/10.1137/s106482750342670x

We outline a probabilistic framework for linking mathematical models to the physical systems that they represent, taking account of all sources of uncertainty including model and simulator imperfections. This framework is a necessary precondition for... Read More about Probabilistic Formulations for Transferring Inferences from Mathematical Models to Physical Systems.

Bayesian forecasting for complex systems using computer simulators (2001)
Journal Article
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

Although computer models are often used for forecasting future outcomes of complex systems, the uncertainties in such forecasts are not usually treated formally. We describe a general Bayesian approach for using a computer model or simulator of a com... Read More about Bayesian forecasting for complex systems using computer simulators.

A Bayesian Analysis of Fluid Flow in Pipelines (2001)
Journal Article
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

The waterhammer equations are a pair of partial differential equations that describe the behaviour of an incompressible fluid in a pipe-line. We generalize these equations to account for uncertainty in the description of the liquid and the pipe-line,... Read More about A Bayesian Analysis of Fluid Flow in Pipelines.