Units

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Sample subject outline - Semester 1 2013

Note: Subject outlines often change before the semester begins. Below is a sample outline.

Rationale

Bayesian statistical inference has a wide range of applications at the professional level including data analytic techniques, multi-level modelling and statistical learning. Thus, it is important that the statistics professional who is proceeding to workplaces where these techniques are used, or is looking for further professional development or who is pursuing research, has a solid grounding in the concepts and methodology of Bayesian statistical inference. This unit is intended to give honours and other postgraduate students a sound basis in the concepts and methodology of Bayesian statistical inference that are used in a wide range of applications at the professional level including data analytic techniques, multi-level modelling and statistical learning. The unit is oriented to enable students to proceed to a variety of workplaces, or to further professional development, or to research.

Aims

As an honours or postgraduate student, the aim of this unit is to give you a sound background in the concepts and methodologies of Bayesian inference.

Objectives

On successful completion of this unit you should have:

1. An advanced knowledge of the theory of Bayesian statistical modelling and inference necessary for advanced applications in economics, scientific, medical, health and engineering problems.
2. An advanced knowledge of computational techniques applicable to Bayesian inference
3. Advanced practical ability to conduct Bayesian inference using various computational platforms such as Splus/R and WinBUGs.
4. An ability to present in written and oral form the motivation, details and results of a Bayesian analysis of a data set.

Content

1. Consolidation and extension of the idea of the likelihood for statistical inference, linking frequentist and objective Bayesian ideas especially in an historical context.
2. Consolidation and extension of the idea of subjective probability.
3. Development of the theory and use of computational methods such as Monte Carlo simulation techniques including importance sampling and Markov chain Monte Carlo.
4. Development of the theory for Bayesian inference including prior specifications,hierarchical models, latent models and subjective priors.
5. Model comparison and goodness of fit evaluation, including Deviance Information Criterion, posterior predictive statistics, posterior predictive residuals and Bayes factors.
6. Bayesian theory of decision making and basics of estimating risk.

Approaches to Teaching and Learning

The approaches aim to facilitate your individual understanding of the key concepts and issues that are common to statistical inference in extended and complex problems and applications. Strategies also aim to assist you to develop responsibility for your own learning within selected contexts that provide demonstration of key issues and methodology. The teaching and learning materials include clear and well-signposted core notes with reference to a text, plus clear and concise computing notes; plus problems in context and case studies to facilitate your discussion and to provide a learning environment in which you can develop your problem-solving skills. This unit has three contact hours per week.

Assessment

You will be required to sit a three hour end of semester exam (40%); hand in two problem solving exercises (25%) , a semester length project (25%) and give an oral presentation on your project (10%).Written feedback on problem solving will be provided during semester, and oral feedback will be given to students on their project at the time of class presentation.

Assessment name: Problem Solving Task
Description: Two problem solving exercises worth 10%, consisting of solutions to selected exercises involving some computation.
Relates to objectives: 1, 2 and 3.
Weight: 25%
Internal or external: Internal
Group or individual: Individual
Due date: Weeks 5 & 9

Assessment name: Presentation (Oral or Group)
Description: In class presentation of project report (Assessment Item 2 above).
Relates to objectives: 4.
Weight: 10%
Internal or external: Internal
Group or individual: Individual
Due date: Week 13

Assessment name: Examination (Theory)
Description: An examination based on the above content presented during the semester.
Relates to objectives: 1, 2 and 3.
Weight: 40%
Internal or external: Internal
Group or individual: Individual
Due date: Exam Period

Assessment name: Research Paper
Description: An in depth analysis of a complex data set which can be based on a student's own research, employment or be available in the scientific literature. A Bayesian analysis should be presented in the report which significantly extends class material in one or more avenues.
Relates to objectives: 1, 2, 3 and 4.
Weight: 25%
Internal or external: Internal
Group or individual: Individual
Due date: Week 13

Academic Honesty

QUT is committed to maintaining high academic standards to protect the value of its qualifications. To assist you in assuring the academic integrity of your assessment you are encouraged to make use of the support materials and services available to help you consider and check your assessment items. Important information about the university's approach to academic integrity of assessment is on your unit Blackboard site.

A breach of academic integrity is regarded as Student Misconduct and can lead to the imposition of penalties.

Resource materials

Lecture notes will be made available on the unit blackboard site.

Reference texts and readings will be referred to throughout the semester, and full bibliographic details will be given during lectures. Some references are:


1. Lee PM (2004) Bayesian Statistics: An Introduction, 3rd Edition, Oxford University Press

2. Congdon P (2006) Bayesian Statistical Modelling, 2nd Edition, New York: Wiley

3. Gelman A, Carlin JB, Stern H & Rubin DB (2004) Bayesian Data Analysis, Second Edition, London: Chapman and Hall

4. Liu JS (2004) Monte Carlo strategies in scientific computing, New York: Springer

Risk assessment statement

There are no out of the ordinary risks associated with this unit. Emergency exits, evacuation procedures and assembly areas will be described in the first few lectures. More information can be found on the university's Health and Safety web site at http://www.hrd.qut.edu.au/healthsafety/healthsafe/index.jsp

Disclaimer - Offer of some units is subject to viability, and information in these Unit Outlines is subject to change prior to commencement of semester.

Last modified: 19-Oct-2012