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Outputs (3)

Bayes linear analysis of risks in sequential optimal design problems (2018)
Journal Article
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

In a statistical or physical model, it is often the case that a set of design inputs must be selected in order to perform an experiment to collect data with which to update beliefs about a set of model parameters; frequently, the model also depends o... Read More about Bayes linear analysis of risks in sequential optimal design problems.

Statistics of extreme ocean environments: Non-stationary inference for directionality and other covariate effects (2016)
Journal Article
Jones, M., Randell, D., Ewans, K., & Jonathan, P. (2016). Statistics of extreme ocean environments: Non-stationary inference for directionality and other covariate effects. Ocean Engineering, 119, 30-46. https://doi.org/10.1016/j.oceaneng.2016.04.010

Numerous approaches are proposed in the literature for non-stationarity marginal extreme value inference, including different model parameterisations with respect to covariate, and different inference schemes. The objective of this paper is to compar... Read More about Statistics of extreme ocean environments: Non-stationary inference for directionality and other covariate effects.

Bayes linear analysis for Bayesian optimal experimental design (2015)
Journal Article
Jones, M., Goldstein, M., Jonathan, P., & Randell, D. (2016). Bayes linear analysis for Bayesian optimal experimental design. Journal of Statistical Planning and Inference, 171, 115-129. https://doi.org/10.1016/j.jspi.2015.10.011

In many areas of science, models are used to describe attributes of complex systems. These models are generally themselves highly complex functions of their inputs, and can be computationally expensive to evaluate. Often, these models have parameters... Read More about Bayes linear analysis for Bayesian optimal experimental design.