Undergraduate Programme and Module Handbook 2018-2019 (archived)
Module MATH4031: BAYESIAN STATISTICS IV
Department: Mathematical Sciences
MATH4031:
BAYESIAN STATISTICS IV
Type |
Open |
Level |
4 |
Credits |
20 |
Availability |
Not available in 2018/19 |
Module Cap |
|
Location |
Durham
|
Prerequisites
- Statistical Concepts II (MATH2041).
Corequisites
Excluded Combination of Modules
- Bayesian Statistics III (MATH3341)
Aims
- To provide an overview of the theory and practice of Bayesian
inference and Bayesian statistical modelling.
Content
- Foundations of Bayesian modelling and inference:
rationality, exchangeability, sufficiency, conjugacy.
- Bayesian statistical modelling: hierarchical models,
Bayesian networks, conditional independence.
- Computation for Bayesian inference: Monte Carlo, Markov
chain Monte Carlo, Gibbs sampling, Metropolis-Hastings.
- Practicalities in Bayesian inference: prior distributions,
interpretation and analysis of MCMC output, model
comparison.
- Reading material in an advanced area of Bayesian
statistics chosen by the lecturer.
Learning Outcomes
- Awareness of a wide range of aspects of Bayesian
statistics.
- A systematic and coherent understanding of the theory,
computation and application of the mathematics underlying the Bayesian
approach to statistics.
- Have acquired a coherent body of knowledge about the
theoretical foundations underpinning the application of Bayesian
statistical inference to scientific and other problems.
- Have acquired a coherent body of knowledge about the
practical application of Bayesian statistical methods.
- Knowledge and understanding obtained by independent study of
a substantial topic in an advanced area of Bayesian
statistics.
- In addition students will have specialised mathematical
skills in the following areas which can be used with minimal guidance:
Modelling, Computation.
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.
- Subject material assigned for independent study develops the
ability to acquire knowledge and understanding without dependence on
lectures.
- 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 |
|
Lectures |
42 |
2 per week for 20 weeks and 2 in term 3 |
1 Hour |
42 |
|
Problems Classes |
8 |
Four in each of terms 1 and 2 |
1 Hour |
8 |
|
Preparation and Reading |
|
|
|
150 |
|
Total |
|
|
|
200 |
|
Summative Assessment
Component: Examination |
Component Weighting: 100% |
Element |
Length / duration |
Element Weighting |
Resit Opportunity |
Written examination |
3 hours |
100% |
none |
Eight written or electronic assignments to be
assessed and returned. Other assignments are set for self-study and
complete solutions are made available to students.
■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University