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The University of Southampton
Southampton Statistical Sciences Research Institute

Bayesian design of experiments and Gaussian process models Seminar

Date:
6 November 2014
Venue:
To be confirmed

Event details

DOE Theme

The design of many experiments can be considered as implicitly Bayesian, with prior knowledge being used informally to aid decisions such as which factors to vary and the choice of plausible causal relationships between the factors and measured responses. Bayesian methods allow uncertainty in such decisions to be incorporated into design selection through prior distributions that encapsulate information available from scientific knowledge or previous experimentation. Further, a design may be explicitly tailored to the aim of the experiment through a decision-theoretic approach with an appropriate loss function.

We will present novel methodology for two problems in this area, related through the application of Gaussian process (GP) regression models. Firstly, we consider Bayesian design for prediction from a GP model, as might be used for the collection of spatial data or for a computer experiment to interrogate a numerical model. Secondly, we address Bayesian design for parametric regression models, and demonstrate the application of GP emulators to mitigate the computational issues that have traditionally been a barrier to the application of these designs.

Speaker information

Professor Dave Woods ,Professor of Statistics

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