The school will provide advanced training in risk and uncertainty in natural hazards from some of the UK’s leading academics. Afternoons will include practical sessions and hands-on exercises.  The school is open to postgraduates, early career researchers and scientists from industry and government agencies.

Registration

Register here. Please note that there are limited spaces so we recommend booking early to avoid disappointment.  Registration closes on Monday 27 June.

Confirmed lecturers and subjects

Calibrate your model – Dr Jonty Rougier

In this session we explore the principles of model calibration, and simple tools for the same: space-filling designs (eg latin hypercubes), visualisation with parallel coordinate plots, dealing with multivariate outputs (principal variables analysis), ruling out regions of parameter space (history matching), proceeding sequentially.

Sensitivity analysis – Professor Thorsten Wagener

Sensitivity analysis investigates how the uncertainty in the model output can be apportioned to the uncertainty in the model input (including its parameters). We will introduce the most widely used approaches to sensitivity analysis and provide hands-on applications of these methods to natural hazard models of varying complexity.

Expert elicitation – Dr Henry Odbert (with Professor Willy Aspinall)

This session covers background to the use of scientific experts’ opinions in decision support; concept of Cooke’s Classical Model for determining performance-based weights and differential pooling of opinions from a group of experts, with strong emphasis on the expression of uncertainty estimates; principles for obtaining a “rational consensus”. Also described will be a complementary approach for eliciting qualitative rankings and preferences by a paired comparison approach coupled with probabilistic inversion to produce ranking metrics and consistency checks.

Decision analysis – Dr Theo Economou 

When trying to fit statistical models for inferential purposes, we may conclude that there is not enough data to actually implement a model. In decision making however, decisions have to be made regardless of the amount or quality of the available information. This session introduces Bayesian decision analysis which offers a coherent framework for making decisions under uncertainty. The framework is illustrated using the example of issuing hazard warnings, followed by an R practical session.

Venue

The summer school will be held at Engineers House, a Grade II listed building in Clifton Down, a prestigious suburb of Bristol.

Directions, travel and parking.

Fees

Postgraduate students: £300

Non-students: £500

The cost includes all course materials & lunch and your place on the course for the week.

Accommodation is not provided but we have a Summer School accommodation list (PDF, 200kB). Please note the University does not endorse these venues.