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Curricular information is subject to change
By the end of the course students should have a good understanding of the key concepts and ideas in Bayesian statistical modelling including, credible intervals; posterior predictive distributions; posterior model checks. Students should be familar also with the idea of Monte Carlo sampling as a means for approximate inference.
Student Effort Type | Hours |
---|---|
Lectures | 24 |
Tutorial | 5 |
Computer Aided Lab | 5 |
Specified Learning Activities | 10 |
Autonomous Student Learning | 75 |
Total | 119 |
You should have completed a basic course in statistics including probability, inference, hypothesis testing, estimation and regression.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Continuous Assessment: Assignments will be a mix of theory and computer based problem sheets. | Throughout the Trimester | n/a | Standard conversion grade scale 40% | No | 30 |
Examination: End of trimester exam. | 2 hour End of Trimester Exam | No | Standard conversion grade scale 40% | No | 70 |
Resit In | Terminal Exam |
---|---|
Autumn | Yes - 2 Hour |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Not yet recorded.