MAS8951 : Modern Bayesian Inference
- Offered for Year: 2022/23
- Module Leader(s): Dr Daniel Henderson
- Lecturer: Dr Murray Pollock
- Owning School: Mathematics, Statistics and Physics
- Teaching Location: Newcastle City Campus
Semesters
| Semester 1 Credit Value: | 15 |
| Semester 2 Credit Value: | 15 |
| ECTS Credits: | 15.0 |
Aims
To develop further an understanding of Bayesian inference, to gain knowledge of modern applications of Bayesian inference and modern Bayesian computational methods and to acquire skill in Bayesian modelling, data analysis and computation.
Module summary
In recent years great advances have been made in the application of Bayesian statistical inference to problems in a wide variety of areas. This has been made possible by the development of computational algorithms which allow posterior distributions to be found in complicated models. This module extends the introductory material on Bayesian inference given in MAS3902 and, in particular, looks at more complex models.
Outline Of Syllabus
Review of Bayesian inference. Review of Markov chain Monte Carlo (MCMC), including Gibbs sampling and Metropolis-Hastings; assessment of mixing and convergence. Theoretical foundations of MCMC. Approximate Bayesian computation. Sequential Monte Carlo methods, including bootstrap particle filter, issues of degeneracy.
Applications, such as: linear models, generalized linear models, missing data problems, data augmentation, mixture models, dynamic linear models, random effects and hierarchical models.
Computation using rjags package within R.
Teaching Methods
Teaching Activities
| Category | Activity | Number | Length | Student Hours | Comment |
|---|---|---|---|---|---|
| Scheduled Learning And Teaching Activities | Lecture | 60 | 1:00 | 60:00 | Formal Lectures – Present in Person |
| Scheduled Learning And Teaching Activities | Lecture | 6 | 1:00 | 6:00 | Revision Lectures – Present in Person |
| Scheduled Learning And Teaching Activities | Lecture | 15 | 1:00 | 15:00 | Problem Classes – Synchronous On-Line |
| Guided Independent Study | Assessment preparation and completion | 40 | 1:00 | 40:00 | Completion of in course assessments |
| Guided Independent Study | Independent study | 179 | 1:00 | 179:00 | Preparation time for lectures, background reading, coursework review |
| Total | 300:00 |
Teaching Rationale And Relationship
Lectures are used for the delivery of theory and explanation of methods, illustrated with examples, and for giving general feedback on marked work. Problem Classes are used to help develop the students’ abilities at applying the theory to solving problems.
Assessment Methods
The format of resits will be determined by the Board of Examiners
Exams
| Description | Length | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|---|
| Written Examination | 150 | 2 | A | 60 | N/A |
Other Assessment
| Description | Semester | When Set | Percentage | Comment |
|---|---|---|---|---|
| Prob solv exercises | 1 | M | 20 | Coursework assignment |
| Prob solv exercises | 2 | M | 20 | Coursework assignment |
Assessment Rationale And Relationship
A substantial formal unseen examination is appropriate for the assessment of the material in this module. The coursework assignments allow the students to develop their problem solving techniques, to practise the methods learnt in the module, to assess their progress and to receive feedback; these assessments have a secondary formative purpose as well as their primary summative purpose.
In the event of on-campus examinations not being possible, an on-line alternative assessment will be used for written examination 1.
Reading Lists
Timetable
- Timetable Website: www.ncl.ac.uk/timetable/
- MAS8951's Timetable