ENBIS-16 in Sheffield11 – 15 September 2016; Sheffield Abstract submission: 20 March – 4 July 2016
Post-Conference Workshop on "Sensitivity Analysis for Computer Models" by Jeremy Oakley and Mark Strong15 September 2016, 09:00 – 12:30
This half-day ENBIS-16 post-conference workshop will be given by Jeremy Oakley and Mark Strong. It can be booked here.
TARGET GROUP: Users of computer models, and statisticians interested in computer model uncertainty. Participants are assumed to have knowledge of standard probability distributions and basic Monte Carlo methods.
ABSTRACT: Computer models are used in many disciplines for making predictions when conducting physical experiments would be too costly or impractical. Typically, there will be uncertainty as to what values should be used for some of the model inputs, which induces uncertainty in the model output predictions. This course is concerned with methods for investigating how individual uncertain inputs contribute to the output uncertainty, so that model users can determine where to concentrate their resources in reducing input uncertainty. The course will include both theory and practical computational tools, implemented in the software package R. The course will also include an introduction to methods for eliciting probability distributions from experts, for the purposes of quantifying uncertainty about model inputs.
OUTLINE: The course will involve a mixture of lectures and computer practicals. Proposed timetable is 9am-10.30am. Sensitivity analysis theory
10.30am-11am. Eliciting prior distributions from experts
11am-12.30pm. Computational methods
TEACHING OBJECTIVES & LEARNING OUTCOMES:
- To understand how to quantify input importance using variance-based and decision-theoretic measures.
- To be able to calculate the measures in (1) for computationally cheap computer models, using R.
- To be able to elicit a univariate probability distribution from an expert.