Undergraduate Programme and Module Handbook 2008-2009 (archived)

Module MATH4191: BAYESIAN METHODS IV

Department: Mathematical Sciences

MATH4191: BAYESIAN METHODS IV

Type	Open	Level	4	Credits	20	Availability	Available in 2009/10 and alternate years thereafter	Module Cap	None.	Location	Durham

Prerequisites

Statistical Concepts II (MATH2041).

Corequisites

None.

Excluded Combination of Modules

Bayesian Methods III (MATH3311).

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.
Reading material in one of the following areas: approximation of posterior distributions, modelling data collection.

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.
knowledge and understanding of a topic in the following areas: approximation of posterior distributions, modelling data collection.

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.

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.
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. The subject material assigned for independent study will form part of the examined material.

Teaching Methods and Learning Hours

Activity	Number	Frequency	Duration	Total/Hours
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: Examination		Component Weighting: 100%
Element	Length / duration	Element Weighting	Resit Opportunity
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.

■ 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