This course covers the fundamental concepts of: Bayesian statistics, including estimation, prediction, hypothesis testing, and decision theory; time series analysis, including estimation and prediction based on ARIMA models; credibility theory, including limited fluctuation credibility theory and the Buhlmann-Straub model; several run-off techniques for estimating an outstanding claims reserve; and Monte Carlo techniques, including the inverse transformation method, the polar method, and Monte Carlo integration.

• Explain the fundamental concepts of Bayesian statistics and use these concepts to calculate Bayesian estimators (including credibility estimators).
• Define and apply the main concepts underlying the analysis of time series models.
• Describe and apply techniques for analysing a delay (or run-off) triangle and projecting the ultimate position.
• Explain and apply the concepts of “Monte Carlo” simulation using a series of pseudo-random numbers.

Mid-semester Exam (30%)

Final Exam (70%)

Students taking this course are expected to commit at least 10 hours a week to completing the work.

STAT2001 Introductory Mathematical Statistics

None.

No computing is required for this course. However, you will need a scientific calculator.

Please refer to Course Website