Students

STAT8150 – Bayesian Data Analysis

2024 – Session 1, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff
Houying Zhu
houying.zhu@mq.edu.au
Benoit Liquet-Weiland
benoit.liquet-weiland@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
STAT6110 or STAT8310
Corequisites Corequisites
Co-badged status Co-badged status
Unit description Unit description

This unit offers a comprehensive introduction to the fundamental concepts and methodologies of Bayesian analysis, highlighting the critical distinctions between Bayesian and frequentist statistical methods. Students will gain an understanding of single-parameter and multi-parameter models and explore various Bayesian computation techniques, including Bayesian regression models such as linear, GLM, and hierarchical models. Emphasis will be placed on the computational aspects of Bayesian data analysis, leveraging modern computational tools and techniques, including Markov Chain Monte Carlo methods like Gibbs sampling and Metropolis sampling. In addition, hands-on experience with statistical software will be provided to enable students to perform analyses effectively.

Important Academic Dates

Information about important academic dates including deadlines for withdrawing from units are available at https://www.mq.edu.au/study/calendar-of-dates

Learning Outcomes

On successful completion of this unit, you will be able to:

  • ULO1: Articulate the fundamental principles of Bayesian statistics and communicate them effectively to a non-expert audience
  • ULO2: Formulate and conduct Bayesian inference procedures, including point and interval estimation and hypothesis testing, for commonly used sampling distributions
  • ULO3: Develop, describe analytically, and implement single-parameter and multi-parameter probability models within the Bayesian framework
  • ULO4: Utilise Bayesian methods and state-of-the-art software tools to solve real-world problems and generate actionable recommendations to support decision-making.

General Assessment Information

Requirements to Pass this Unit

To pass this unit you must:

  • Attempt all assessments, and
  • Achieve a total mark equal to or greater than 50%

Late Assessment Submission Penalty 

Unless a Special Consideration request has been submitted and approved, a 5% penalty (of the total possible mark of the task) will be applied for each day a written report or presentation assessment is not submitted, up until the 7th day (including weekends). After the 7th day, a grade of ‘0’ will be awarded even if the assessment is submitted. The submission time for all uploaded assessments is 11:55 pm. A 1-hour grace period will be provided to students who experience a technical concern.

For any late submission of time-sensitive tasks, such as assessments and presentations, please apply for Special Consideration. 

Assessments where Late Submissions will be accepted

Report 1 – YES, Standard Late Penalty applies 

Report 2 – YES, Standard Late Penalty applies 

Media Presentation  – YES, Standard Late Penalty applies

Final Exam – NO, unless Special Consideration is granted

Special Consideration

The Special Consideration Policy aims to support students who have been impacted by short-term circumstances or events that are serious, unavoidable and significantly disruptive and which may affect their performance in assessment. If you experience circumstances or events that affect your ability to complete the assessments in this unit on time, please inform the convenor and submit a Special Consideration request through ask.mq.edu.au.

Assessment Tasks

Name Weighting Hurdle Due
Report 1 15% No Week 5
Report 2 15% No Week 8
Media presentation 20% No Week 12
Final Exam 50% No Exam Period

Report 1

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 20 hours
Due: Week 5
Weighting: 15%

 

Written assignment

 
On successful completion you will be able to:
  • Articulate the fundamental principles of Bayesian statistics and communicate them effectively to a non-expert audience
  • Formulate and conduct Bayesian inference procedures, including point and interval estimation and hypothesis testing, for commonly used sampling distributions
  • Develop, describe analytically, and implement single-parameter and multi-parameter probability models within the Bayesian framework

Report 2

Assessment Type 1: Quantitative analysis task
Indicative Time on Task 2: 20 hours
Due: Week 8
Weighting: 15%

 

Written assignment

 
On successful completion you will be able to:
  • Articulate the fundamental principles of Bayesian statistics and communicate them effectively to a non-expert audience
  • Formulate and conduct Bayesian inference procedures, including point and interval estimation and hypothesis testing, for commonly used sampling distributions
  • Develop, describe analytically, and implement single-parameter and multi-parameter probability models within the Bayesian framework

Media presentation

Assessment Type 1: Media presentation
Indicative Time on Task 2: 13 hours
Due: Week 12
Weighting: 20%

 

Pre-recorded Media presentation

 
On successful completion you will be able to:
  • Articulate the fundamental principles of Bayesian statistics and communicate them effectively to a non-expert audience
  • Develop, describe analytically, and implement single-parameter and multi-parameter probability models within the Bayesian framework
  • Utilise Bayesian methods and state-of-the-art software tools to solve real-world problems and generate actionable recommendations to support decision-making.

Final Exam

Assessment Type 1: Examination
Indicative Time on Task 2: 2 hours
Due: Exam Period
Weighting: 50%

 

Formal invigilated examination testing the learning outcomes of the unit.

 
On successful completion you will be able to:
  • Articulate the fundamental principles of Bayesian statistics and communicate them effectively to a non-expert audience
  • Formulate and conduct Bayesian inference procedures, including point and interval estimation and hypothesis testing, for commonly used sampling distributions
  • Develop, describe analytically, and implement single-parameter and multi-parameter probability models within the Bayesian framework
  • Utilise Bayesian methods and state-of-the-art software tools to solve real-world problems and generate actionable recommendations to support decision-making.

1 If you need help with your assignment, please contact:

  • the academic teaching staff in your unit for guidance in understanding or completing this type of assessment
  • the Writing Centre for academic skills support.

2 Indicative time-on-task is an estimate of the time required for completion of the assessment task and is subject to individual variation

Delivery and Resources

Classes

Lectures (beginning in Week 1): A two-hour lecture each week.

SGTA classes (beginning in Week 2): Students must register for the SGTA class.

The timetable for classes can be found on the University website at: http://www.timetables.mq.edu.au

Enrolment can be managed using eStudent at: https://students.mq.edu.au/support/technology/systems/estudent.

Computing and Software

R and Rstudio: These are freely available to download from the web and will be used for data analysis in this unit.

Suggested Textbook

  • Peter Hoff. A First Course in Bayesian Statistical Methods. Springer Texts in Statistics, Springer, 2009.
  • Reich, Brian J. and Ghosh Sujit K. Bayesian Statistical Methods. Chapman and Hall/ CRC, 2019.
  • 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. 

Method of Communication

We will communicate with you via your university email or through announcements on iLearn. Queries to convenors can be placed on the iLearn discussion forum. 

COVID Information

For the latest information on the University’s response to COVID-19, please refer to the Coronavirus infection page on the Macquarie website: https://www.mq.edu.au/about/coronavirus-faqs. Remember to check this page regularly in case the information and requirements change during the semester. If there are any changes to this unit in relation to COVID, these will be communicated via iLearn.

Unit Schedule

Week 1: Introduction to Bayesian Analysis. 

Week 2: Prior Specification. How to set a prior distribution in Bayesian statistics? Conjugate priors, improper priors and Jeffrey’s priors. 

Week 3: One Parameter Model. Binomial model and Poisson model, obtain posterior distribution and make the inference. 

Week 4: Introduction to Monte Carlo Method. 

Week 5: Multi-parameter Models. Normal model and make inferences for mean, variance, and posterior predictive checks. 

Week 6: Hierarchical Modelling. 

Week 7: Markov Chain Monte Carlo (Part One). Gibbs sampling and convergence diagnostics

Week 8: Markov Chain Monte Carlo (Part Two). Metropolis sampling and tuning parameters in Metropolis samplers. 

Week 9: Bayesian Linear Regression. 

Week 10: Bayesian Generalized Linear Models. 

Week 11: Model Comparison. Hypothesis testing and Bayes factors cross-validation and information criteria.

Week 12: Frequentist Properties of Bayesian Methods.  

 

Policies and Procedures

Macquarie University policies and procedures are accessible from Policy Central (https://policies.mq.edu.au). Students should be aware of the following policies in particular with regard to Learning and Teaching:

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.

Student Code of Conduct

Macquarie University students have a responsibility to be familiar with the Student Code of Conduct: https://students.mq.edu.au/admin/other-resources/student-conduct

Results

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 connect.mq.edu.au or if you are a Global MBA student contact globalmba.support@mq.edu.au

Academic Integrity

At Macquarie, we believe academic integrity – honesty, respect, trust, responsibility, fairness and courage – is at the core of learning, teaching and research. We recognise that meeting the expectations required to complete your assessments can be challenging. So, we offer you a range of resources and services to help you reach your potential, including free online writing and maths support, academic skills development and wellbeing consultations.

Student Support

Macquarie University provides a range of support services for students. For details, visit http://students.mq.edu.au/support/

The Writing Centre

The Writing Centre provides resources to develop your English language proficiency, academic writing, and communication skills.

The Library provides online and face to face support to help you find and use relevant information resources. 

Student Services and Support

Macquarie University offers a range of Student Support Services including:

Student Enquiries

Got a question? Ask us via the Service Connect Portal, or contact Service Connect.

IT Help

For help with University computer systems and technology, visit http://www.mq.edu.au/about_us/offices_and_units/information_technology/help/. 

When using the University's IT, you must adhere to the Acceptable Use of IT Resources Policy. The policy applies to all who connect to the MQ network including students.

Changes from Previous Offering

We value student feedback to be able to continually improve the way we offer our units. As such we encourage students to provide constructive feedback via student surveys, to the teaching staff directly, or via the FSE Student Experience & Feedback link in the iLearn page.

Student feedback from the previous offering of this unit was very positive overall, with students pleased with the clarity around assessment requirements and the level of support from teaching staff.  We will continue to strive to improve the level of support and the level of student engagement. To better assess students' understanding of the contents of this unit, as suggested by the external review panel, an examination has been added to this offering. 

Unit information based on version 2024.03 of the Handbook