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Description
This module aims to provide an introduction to the ideas underlying the calculation of risk from a Bayesian and frequentist standpoint, and the structure of rational, consistent decision making. It is primarily intended for second and third year undergraduates registered on the degree programmes offered by the Department of Statistical Science (including the MASS programmes). It also serves as an optional module for students taking the Mathematics and Statistics stream of the Natural Sciences degree.听For all these students, the academic prerequisites for this module are met through compulsory study earlier in their programme.
Intended Learning Outcomes
- have an understanding of the concepts of statistical decision theory;
- be able to understand and compute financial measures of risk;
- be able to find appropriate probability models for risky events and check the validity of the underlying assumptions.
Applications - The ideas introduced in this module provide a generic framework for thinking about risk and decision-making in the presence of uncertainty. As such, they can be applied in many diverse areas. The module will include examples from natural hazards and finance using the R statistical software package.
Indicative Content - Introduction to Bayesian inference: conditional probability, Bayes' theorem, elicitation of subjective probabilities. Statistical decision theory from Bayesian and frequentist standpoints: expected loss, randomised and non-randomised decision rules, decision principles, comparison of decision rules. Measures of risk: Value-at-Risk, Expected Shortfall. Dependence modelling: use of copulas and models for time series. Extreme value theory: probability models for the occurrence of extreme events.
Key Texts - Available from .
Module deliveries for 2024/25 academic year
Last updated
This module description was last updated on 8th April 2024.
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