This is an archived handbook for a previous year. The current undergraduate and postgraduate handbooks can be viewed using the links above.
Undergraduate Handbook 2006-2007 (archived)
Module MATH3311: BAYESIAN METHODS III
Department: Mathematical Sciences
MATH3311: BAYESIAN METHODS III
| Type | Level | Credits | Availability | Module Cap | Location |
|---|---|---|---|---|---|
| Open | 3 | 20 | Not available in 2006/2007 | None. | Durham |
Pre-requisites
- (Statistical Concepts II (MATH2041) AND one extra 20 credit Level 2 mathematics module) OR (Statistical Concepts II (MATH2041) and Core Mathematics B1 (if taken in Year 2)).
Corequisites
- One 20 credit Level 3 mathematics module.
Excluded Combinations
- Bayesian Methods IV (MATH4191)
Aims
- To provide an overview of practical Bayesian statistical methodology together with important applications.
Content
- Conditional independence
- Bayesian graphical modelling
- Elicitation of beliefs
- Random number generation
- Markov chain Monte Carlo simulation
- Analysis of MCMC output.
Learning Outcomes
Subject-specific Knowledge
- An awareness of the abstract concepts of theoretical mathematics in the field of Bayesian methods.
- knowledge and understanding of fundamental theories of these subjects demonstrated through one or more of the following topic areas: Conditional independence
- Bayesian graphical models
- Belief elicitation
- Random number generation
- Markov chain Monte Carlo simulation
- Analysis of MCMC output.
- knowledge and understanding of important applications of Bayesian methods in other disciplines.
Subject-specific Skills
- Ability to solve a range of predictable and unpredictable problem solving Bayesian methods for statistical inference.
- 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.
- In addition, formatively assessed assignments provide feedback for students and the lecturer on student progress and opportunities for the lecturer to test and enhance development of modelling and computation skills.
- Summative examination assesses acquired knowledge, problem-solving skills and arrange of modelling and computational skills.
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 | 3 hours | 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.