Curricular information is subject to change.
By the end of this module you will be familiar with the theory and implementation of advanced Bayesian inferential methods that are optimised to explore posterior distributions effectively, including when the likelihood is intractable. You will know which approaches to use and why and will have experience in fitting complex models to highly heterogeneous / structured data.
| Student Effort Type | Hours |
|---|---|
| Autonomous Student Learning | 76 |
| Lectures | 24 |
| Total | 100 |
Students will require prior knowledge of Bayesian Statistics and also an advanced understanding of probability and statistics.
| Description | Timing | Component Scale | % of Final Grade | ||
|---|---|---|---|---|---|
| Assignment(Including Essay): Assignment due in Week 5 | Week 1, Week 2, Week 3, Week 4, Week 5 | Alternative linear conversion grade scale 40% | No | 50 |
No |
| Assignment(Including Essay): Assignment due in week 9 | Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9 | Alternative linear conversion grade scale 40% | No | 50 |
No |
| Resit In | Terminal Exam |
|---|---|
| Autumn | No |
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
Not yet recorded.
| Name | Role |
|---|---|
| Moisés Chavira Flores | Tutor |