Notice
As part of Phase 3 of our return to campus plan, most units will now run tutorials, seminars and other small group activities on campus, and most will keep an online version available to those students unable to return or those who choose to continue their studies online.
To check the availability of face-to-face and online activities for your unit, please go to timetable viewer. To check detailed information on unit assessments visit your unit's iLearn space or consult your unit convenor.
Unit convenor and teaching staff |
Unit convenor and teaching staff
Lecturer and Convenor
Benoit Liquet-Weiland
Contact via benoit.liquet-weiland@mq.edu.au
Room 630, 12WW
TBA
Lecturer and Convenor
Houying Zhu
Contact via houying.zhu@mq.edu.au
Room 705, 12WW
TBA
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Credit points |
Credit points
10
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Prerequisites |
Prerequisites
((Admission to MAppStat or GradCertAppStat or GradDipAppStat or MDataSc or MActPrac) and (STAT806 or STAT810 or STAT6110 or STAT8310)) or (Admission to BMathScMAppStat and permission by special approval)
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Corequisites |
Corequisites
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Co-badged status |
Co-badged status
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Unit description |
Unit description
This unit introduces main concepts and methods of Bayesian analysis with a clear comparison with frequentist statistical methods. Both single-parameter and multi-parameter models are derived. Bayesian computation techniques and Bayesian regression models, which include linear, GLM and hierarchical models, are studied in the unit. This unit highlights and exploits computational aspects of Bayesian data analysis including Markov Chain Monte Carlo (MCMC) methods (Gibbs sampling, Hastings-Metropolis) using the latest computational tools. |
Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates
On successful completion of this unit, you will be able to:
Name | Weighting | Hurdle | Due |
---|---|---|---|
Report 1 | 30% | No | Week 5 |
Report 2 | 30% | No | Week 8 |
Report 3 | 30% | No | Week 12 |
Media presentation | 10% | No | Week 13 |
Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 20 hours
Due: Week 5
Weighting: 30%
The report will focus mainly on the material covered in Lecture Weeks 1-3.
Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 20 hours
Due: Week 8
Weighting: 30%
The report will focus mainly on the material covered in Lecture Weeks 4-6.
Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 20 hours
Due: Week 12
Weighting: 30%
The report will focus mainly on the material covered in Lecture Weeks 7-10.
Assessment Type 1: Media presentation
Indicative Time on Task 2: 13 hours
Due: Week 13
Weighting: 10%
Students are required to produce a media presentation demonstrating a unit topic of their choice. This demonstration needs to be a brief and accessible to other students that haven't studied the specific topic but have similar Mathematics/Statistics background.
1 If you need help with your assignment, please contact:
2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation
All lectures are online. They will be delivered as a combination of live zoom classes and prerecorded video recordings. Please refer to iLearn for more details.
Teaching Activities (SGTAs) SGTA classes will start in week 2.
- Peter Hoff, A First Course in Bayesian Statistical Methods, Springer Texts in Statistics
- Lambert B. A Student’s Guide to Bayesian Statistics. SAGE Publications Ltd, 2018.
- Kruschke JK. Doing Bayesian Data Analysis: A Tutorial with R, JAGS and Stan. Academic Press / Elsevier, 2015.
- McElreath R. Statistical Rethinking: A Bayesian Course with Examples in R and Stan, CRC Press / Taylor and Francis / Chapman and Hall, 2016.
- Gelman A, Carlin JB, Stern HS, Dunson DB, Vehtari A, Rubin DB. Bayesian Data Analysis (3rd Edition). CRC Press / Taylor and Francis / Chapman and Hall, 2014.
R and Rstudio: These are freely available to download from the Web, and they will be used for data analysis in this unit
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Students seeking more policy resources can visit Student Policies (https://students.mq.edu.au/support/study/policies). It is your one-stop-shop for the key policies you need to know about throughout your undergraduate student journey.
To find other policies relating to Teaching and Learning, visit Policy Central (https://policies.mq.edu.au) and use the search tool.
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Results published on platform other than eStudent, (eg. iLearn, Coursera etc.) or released directly by your Unit Convenor, are not confirmed as they are subject to final approval by the University. Once approved, final results will be sent to your student email address and will be made available in eStudent. For more information visit ask.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au
Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/
Learning Skills (mq.edu.au/learningskills) provides academic writing resources and study strategies to help you improve your marks and take control of your study.
The Library provides online and face to face support to help you find and use relevant information resources.
Students with a disability are encouraged to contact the Disability Service who can provide appropriate help with any issues that arise during their studies.
For all student enquiries, visit Student Connect at ask.mq.edu.au
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Unit information based on version 2021.05 of the Handbook