This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.
Undergraduate Handbook 2005-2006 (archived)
Module MATH4031: BAYESIAN STATISTICS IV
Department: Mathematical Sciences
MATH4031: BAYESIAN STATISTICS IV
| Type | Level | Credits | Availability | Module Cap | Location |
|---|---|---|---|---|---|
| Open | 4 | 20 | Available in 2005/2006 | None. | Durham |
Pre-requisites
- Statistical Concepts II (MATH2041).
Corequisites
- None.
Excluded Combinations
- Bayesian Statistics III (MATH3**1).
Aims
- To provide an overview of the theoretical basis for Bayesian statistics, and to offer various substantial and important practical applications of the Bayesian approach.
Content
- The Bayesian Approach: Discussion of Bayesian foundations.
- Bayesian Forecasting.
- Markov Chain Monte Carlo techniques for Bayesian computations, Bayesian graphical models, Bayes linear methods.
Learning Outcomes
Subject-specific Knowledge
- By the end of the module students will: be able to solve complex, unpredictable and specialised problems in Bayesian Statistics.
- have an understanding of specialised and complex theoretical mathematics in the field of Bayesian Statistics.
- have mastered a coherent body of knowledge of these subjects demonstrated through one or more of the following topic areas: subjective probability and foundations of Bayesian statistics.
- Exchangeability and hierarchical modelling.
- Conditional independence and Bayesian graphical models.
- Monte Carlo inference including Gibbs sampling.
- Bayes linear methods.
Subject-specific Skills
- In addition students will have highly specialised and advanced mathematical skills in the following areas: Modelling, Computation.
Key Skills
Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module
- Lectures demonstrate what is required to be learned and the application of the theory to practical examples.
- Assignments for self-study develop problem-solving skills and enable students to test and develop their knowledge and understanding.
- Formatively assessed assignments provide practice in the application of logic and high level of rigour as well as feedback for the students and the lecturer on students' progress.
- The end-of-year examination assesses the knowledge acquired and the ability to solve complex and specialised problems.
Teaching Methods and Learning Hours
| Activity | Number | Frequency | Duration | Total Hours | Attendance Monitored |
|---|---|---|---|---|---|
| Lectures | 40 | 2 per week for 19 weeks and 2 in term 3 | 1 Hour | 40 | |
| Preparation and Reading | 160 | ||||
| Total | 200 |
Summative Assessment
| Component | Component Weighting | Element | Length/Duration | Element Weighting | Resit Opportunity |
|---|---|---|---|---|---|
| Examination | 100% | three hour written examination | 100% |
Formative Assessment
Four written assignments to be assessed and returned. Other assignments are set for self-study and complete solutions are made available to students.
Students who do not attend monitored activities shown under Teaching Methods
and Learning Hours, or who fail to complete the summative or formative
assessment(s) specified above, may be subject to the Academic Progress
procedures defined in the University's General Regulation V, and may be
required to leave the University.