Undergraduate: Level 6
Thursday 03 October 2019
Saturday 14 December 2019
01 October 2019
Requisites for this module
MA108 and (MA200 or MA207)
BSC N233 Actuarial Science (Including Placement Year),
BSC N323 Actuarial Science,
BSC N324 Actuarial Science (Including Year Abroad),
BSC N325 Actuarial Science (Including Foundation Year),
BSC 5B43 Statistics (Including Year Abroad),
BSC 9K12 Statistics,
BSC 9K13 Statistics (Including Placement Year),
BSC 9K18 Statistics (Including Foundation Year)
The module introduces decision theory, loss distributions, risk modelling, "Monte Carlo" simulation, Bayesian inference, comparative inference and the generalised linear model.
Loss, risk, admissible and inadmissible decisions, randomised decisions. Minimax decisions and Bayes' solutions, including simple results.
Explain the concepts of decision theory and apply them. Calculate probabilities and moments of loss distributions both with and without limits and risk-sharing arrangements. Construct risk models involving frequency and severity distributions and calculate the moment generating function and the moments for the risk models both with and without simple reinsurance arrangements. Explain the concept of ruin for a risk model. Calculate the adjustment coefficient and state Lundberg's inequality. Describe the effect on the probability of ruin of changing parameter values and of simple reinsurance arrangements. Describe and apply techniques for analysing a delay (or run-off) triangle and projecting the ultimate position.
Prior and posterior distributions. Choice of prior: bets, conjugate families of distributions, vague and improper priors. Predictive distributions. Bayesian estimates and intervals for parameters and predictions. Bayes factors and implications for hypothesis tests. Use of Monte Carlo simulation of the posterior distribution to draw inferences. Bayesian and Empirical Bayes approach to credibility theory and use it to derive credibility premiums in simple cases.
Different criteria for choosing good estimators, tests and confidence intervals. Different approaches to inference, including classical, Bayesian and non-parametric.
Generalised linear model
Explain the fundamental concepts of a generalised linear model (GLM), and describe how a GLM may apply.
On completion of the module students should be able to (learning outcomes):
Understand concepts of decision theory;
Apply concepts of decision theory (risk models);
Understand techniques for analysing a delay (or run-off) triangle and projecting the ultimate position;
Understand "Monte-Carlo" simulation;
Understand basic principles of Bayesian inference;
Understand principles and methods to choose good estimators;
Understand basic concepts of a generalised linear model.
No additional information available.
The module consist of 26 lectures, 4 classes. In the summer term 3 revision lectures are given.
This module does not appear to have any essential texts. To see non-essential items, please refer to the module's reading list.
Assessment items, weightings and deadlines
|Coursework / exam
||180 minutes during Summer (Main Period) (Main)
Module supervisor and teaching staff
Dr Yanchun Bao (email@example.com)
Dr Yanchun Bao (firstname.lastname@example.org)
Dr Dimitrina Dimitrova
Cass Business School, City, University of London
Available via Moodle
Of 40 hours, 40 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).
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