Postgraduate Course: Bayesian Theory (MATH11177)
Course Outline
| School | School of Mathematics |
College | College of Science and Engineering |
| Credit level (Normal year taken) | SCQF Level 11 (Postgraduate) |
Availability | Not available to visiting students |
| SCQF Credits | 10 |
ECTS Credits | 5 |
| Summary | This course will provide the underlying theory for Bayesian statistics. Students will understand the role of the prior distribution and be able to formulate a posterior distribution (up to proportionality). They will also be exposed to the ideas of summarising posterior distributions and associated statistics. The underlying theory of Markov chain Monte Carlo will be introduced (Metropolis-Hastings and Gibbs sampler) with associated issues of implementation. |
| Course description |
1. Bayes theorem (discrete and continuous)
2. Prior specification
3. Posterior distribution and associated summary statistics (point and interval)
4. Predictive distributions
5. Hypothesis testing (simple and composite hypotheses)
6. Monte Carlo integration
7. Markov chain Monte Carlo (MCMC): Metropolis-Hastings (MH) and Gibbs sampler
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Course Delivery Information
|
| Academic year 2018/19, Not available to visiting students (SS1)
|
Quota: None |
| Course Start |
Semester 1 |
Timetable |
Timetable |
| Learning and Teaching activities (Further Info) |
Total Hours:
100
(
Lecture Hours 20,
Seminar/Tutorial Hours 5,
Programme Level Learning and Teaching Hours 2,
Directed Learning and Independent Learning Hours
73 )
|
| Assessment (Further Info) |
Written Exam
90 %,
Coursework
10 %,
Practical Exam
0 %
|
| Additional Information (Assessment) |
Coursework 10%, Examination 90% |
| Feedback |
Not entered |
| Exam Information |
| Exam Diet |
Paper Name |
Hours & Minutes |
|
| Main Exam Diet S1 (December) | Bayesian Theory (MATH11177) | 2:00 | |
Learning Outcomes
On completion of this course, the student will be able to:
- Understand the Bayesian philosophy.
- Specify a prior distribution and derive a posterior distribution (up to proportionality).
- Derive additional related posterior distributions and quantities algebraically.
- Understand the theory of Markov Chain Monte Carlo (MCMC) and associated implementation issues.
|
Reading List
Applied Bayesian Statistics, With R and OpenBUGS Examples, M. Cowles. (2013) Springer.
Bayesian Methods for Data Analysis, 3rd Ed. Carlin and Louis. (2009) CRC Press.
Introducing Monte Carlo Methods with R. Robert and Casella (2010) Springer.
Bayesian statistics : an introduction 4th Ed. P.M. Lee. (2012) Wiley.
Bayesian Data Analysis, 3rd Ed. Gelman, Carlin, Stern, Dunson, Vehtari, and Rubin. (2013) CRC Press.
Markov chain Monte Carlo: Stochastic simulation for Bayesian Inference, 2nd Ed. Gamerman and Lopes. (2006) CRC Press. |
Additional Information
| Graduate Attributes and Skills |
Not entered |
| Keywords | BTh,statistics,Bayesian,Markov chain Monte Carlo |
Contacts
| Course organiser | Dr Ken Newman
Tel: (0131 6)50 4899
Email: ken.newman@ed.ac.uk |
Course secretary | Miss Gemma Aitchison
Tel: (0131 6)50 9268
Email: Gemma.Aitchison@ed.ac.uk |
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