University of Sheffield

Tony O'Hagan - Academic pages


Short course: Uncertainty in Mechanistic Models

This course provides an introduction to Bayesian methods, based particularly on the use of Gaussian process emulators, to characterise uncertainties in the outputs of mechanistic models. No background knowledge is assumed beyond very basic appreciation of statistical methods and of how mechanistic models are used to predict complex real-world processes. This is a hands-on course with exercises using the GEM-SA software. Participants will be expected to have their own laptop computers.

Learning Objectives

At the end of the course, participants should:


The course is structured as 7 sessions. Sessions with GEM-SA in their title are hands-on sessions, each involving a walk-through example and one or more practical exercises. Session topics are as follows.
  1. Uncertainty in computer models. Mechanistic models of real-world processes. Sources of uncertainty. Uncertainty and sensitivity analyses. Computational issues.
  2. Gaussian process modelling. Simulators and emulators. The Gaussian process emulator. Mean and covariance modelling. Design and validation.
  3. Getting started with GEM-SA. Introduction to the software and principal options/menus. Creating a project.
  4. Uncertainty analysis using GEM-SA. Input and output files. Fitting and uncertainty analysis. Validation diagnostics.
  5. Elicitation of input distributions. Eliciting expert knowledge. The SHELF system. Practical exercise.
  6. Sensitivity analysis in GEM-SA. Setting up and interpreting sensitivity analyses. Main, interaction and total effects.
  7. More advanced topics. Structural uncertainty. Calibration and data assimilation. Dynamic and stochastic simulators. Multiple outputs.

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Updated: 1 November 2009
Maintained by: Tony O'Hagan