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Curricular information is subject to change
The student will be able to define and classify stochastic models, judge when a model is satisfactory and be able to outline how to construct a stochastic model. The student will be able to apply Markov chains and Markov jump processes to actuarial applications, solving for their short run and long run behaviour. The student will be able to calibrate models from data, test the appropriateness of models, judge their overall suitability and simulate their behaviour.Indicative Module Content:
|Student Effort Type||Hours|
|Autonomous Student Learning||
The student will have an aptitude for mathematics, with a profligacy to 1st years honours level, and have taken level 2 modules in statistics.Learning Recommendations:
Knowledge of probability theory and basic statistics to the level of the Probability Theory (STAT20110) and Inferential Statistics (STAT20100) course. Good knowledge in calculus (integrals, differentials) and linear algebra (vectors, matrices, eigenvalues).
|Description||Timing||Component Scale||% of Final Grade|
|Examination: Mid-term exam||Week 7||No||Alternative linear conversion grade scale 40%||No||
|Examination: Final Examination||2 hour End of Trimester Exam||No||Alternative linear conversion grade scale 40%||No||
|Resit In||Terminal Exam|
|Spring||Yes - 2 Hour|
• Group/class feedback, post-assessment
Not yet recorded.
|Mr Matt Nagle||Tutor|