UNIVERSITY OF KENT
Confirmation that this version of the module specification has been approved by the School
Learning and Teaching Committee:
……15th December 2014……………….(date)
MODULE SPECIFICATION
1.
Title of the module
Applied Bayesian Modelling (MA538)
2.
School or partner institution which will be responsible for management of the
module
School of Mathematics, Statistics and Actuarial Science
3.
Start date of the module
September 2015
4.
The number of students expected to take the module
40
5.
Modules to be withdrawn on the introduction of this proposed module and
consultation with other relevant Schools and Faculties regarding the withdrawal
None
6.
The level of the module (e.g. Certificate [C], Intermediate [I], Honours [H] or
Postgraduate [M])
Honours [H]
7.
The number of credits and the ECTS value which the module represents
15 (7.5 ECTS)
8.
Which term(s) the module is to be taught in (or other teaching pattern)
Autumn term
9.
Prerequisite and co-requisite modules
Prerequisite module: MA629 Probability and Inference
There are no co-requisite modules.
10. The programmes of study to which the module contributes
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BSc (Hons) Mathematics, BSc (Hons) Mathematics and Statistics (including programmes
with a year in industry), BSc (Hons) Mathematics with a Foundation Year, MMath
Mathematics
11. The intended subject specific learning outcomes
On successful completion of this module, students will:
a. be able to derive posterior distributions when analytically tractable;
b. understand how to derive posterior summaries, such as estimates, from the
posterior distribution, including the predictive distribution;
c. be able to construct Bayesian hierarchical models and implement them in a
suitable software package;
d. be able to critically evaluate software output using convergence diagnostics;
e. be able to interpret and report the output for inferential purposes.
12. The intended generic learning outcomes
On successful completion of this module, students will:
a. have developed conceptual skills and logical reasoning ability;
b. have enhanced their ability to work with relatively little guidance;
c. have acquired key skills in written communication, numeracy and problem
solving.
13. A synopsis of the curriculum
Bayes Theorem for density functions; Exchangeability; Choice of priors; Conjugate models;
Predictive distribution; Bayes estimates; Sampling density functions; Gibbs samplers;
OpenBUGS; Bayesian hierarchical models; Applications of hierarchical models; Bayesian
model choice.
14. Indicative Reading List
A. Gelman, J.B. Carlin, H.S. Stern, D.B. Dunson, A. Vehtari and D.B. Rubin (2014).
Bayesian Data Analysis. 3rd Edition, Chapman & Hall/CRC Texts in Statistical Science
D. Gamerman and H.F. Lopes (2006). Markov Chain Monte Carlo: Stochastic
Simulation for Bayesian Inference. 2nd Edition, Taylor and Francis.
15. Learning and Teaching Methods, including the nature and number of contact hours
and the total study hours which will be expected of students, and how these relate to
achievement of the intended module learning outcomes
Number of contact hours:
36, consisting of 30 lectures and 6 classes (1 hour each)
Independent learning hours:
114
Total study hours:
150.
The lectures contain numerous worked examples to emphasise the practical application
of the theory. There are two written assessments that count towards the module mark;
at least one assessment will cover the use of OpenBUGS or a similar program.
Examples and assessed exercises are intended to reinforce the lecture material and to
apply the concepts taught to practical problems.
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Independent learning hours would be distributed between consolidation of lecture
material, the working of exercises on exercise sheets, assessed exercises, and exam
preparation.
Lectures and classes will cover learning outcomes 11(a)-(e) and 12(a)-(c).
16. Assessment methods and how these relate to testing achievement of the intended
module learning outcomes
Assessment: This module is assessed by examination (80%) and by coursework (20%).
Coursework: This will consist of two open-book, written assessments which are
completed by students outside contact hours. These consist of questions and numerical
problems which will test learning outcomes 11(a)-(e) and 12(a)-(c).
Examination: A 2-hour written examination in the Summer term that will consist of multipart questions requiring a mix of long and short answers to test at varying levels the
proficiency of the learning outcomes 11(a)-(e) and 12(a)-(c).
17. Implications for learning resources, including staff, library, IT and space
The module will be taught by an existing member of staff from the Statistics Group of the
School of Mathematics, Statistics and Actuarial Science. The recommended texts are
currently available in the library. Therefore there should be minimal overall impact.
18. The School recognises and has embedded the expectations of current disability
equality legislation, and supports students with a declared disability or special
educational need in its teaching. Within this module we will make reasonable
adjustments wherever necessary, including additional or substitute materials,
teaching modes or assessment methods for students who have declared and
discussed their learning support needs. Arrangements for students with declared
disabilities will be made on an individual basis, in consultation with the University’s
disability/dyslexia support service, and specialist support will be provided where
needed.
19. Campus(es) or Centre(s) where module will be delivered:
Canterbury
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