Students

STAT8150 – Bayesian Data Analysis

2026 – Session 1, In person-scheduled-weekday, North Ryde

General Information

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Unit convenor and teaching staff Unit convenor and teaching staff
Karol Binkowski
karol.binkowski@mq.edu.au
Credit points Credit points
10
Prerequisites Prerequisites
STAT6110 or STAT6191 or STAT8310 or (Admission to GradCertResFSE or GradDipResFSE)
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.

Learning in this unit enhances student understanding of global challenges identified by the United Nations Sustainable Development Goals (UNSDGs) Industry, Innovation and Infrastructure

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 need to:

Achieve a total mark equal to or greater than 50% across all assessments.

Hurdle Assessments

  • There is no Hurdle Assessment in this unit.

Attendance and participation

We strongly encourage all students to actively participate in all learning activities. Regular engagement is crucial for your success in this unit, as these activities provide opportunities to deepen your understanding of the material, collaborate with peers, and receive valuable feedback from instructors, to assist in completing the unit assessments. Your active participation not only enhances your own learning experience but also contributes to a vibrant and dynamic learning environment for everyone.

Late Assessment Submission Penalty

  • 5% penalty per day: If you submit your assessment late, 5% of the total possible marks will be deducted for each day (including weekends), up to 7 days.
    • Example 1 (out of 100): If you score 85/100 but submit 20 hours late, you will lose 5 marks and receive 80/100.
    • Example 2 (out of 30): If you score 27/30 but submit 1 day late, you will lose 1.5 marks and receive 25.5/30.
  • After 7 days: Submissions more than 7 days late will receive a mark of 0.
  • Extensions:
    • Automatic short extension: Some assessments are eligible for automatic short extension. You can only apply for an automatic short extension before the due date.
    • Special Consideration: If you need more time due to serious issues and for any assessments that are not eligible for Short Extension, you must apply for Special Consideration.

Need help? Review the Special Consideration page HERE

For any late submission of time-sensitive tasks, such as scheduled tests/exams,  please apply for Special Consideration.

Assessments where Late Submissions will be accepted

  • Written report – 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 https://connect.mq.edu.au.

 

Assessment Tasks

Name Weighting Hurdle Due Groupwork/Individual Short Extension AI assisted?
Written report 20% No 05/04/2026 Individual No
Media presentation 30% No 31/05/2026 Individual No
Final Exam 50% No Formal examination period Individual No

Written report

Assessment Type 1: Written Submission
Indicative Time on Task 2: 23 hours
Due: 05/04/2026
Weighting: 20%
Groupwork/Individual: Individual
Short extension 3: No
AI assisted?:

You will write a report communicating the results of a Bayesian analysis, demonstrating your understanding of priors, posterior inference, and interpretation.
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: Presentation task
Indicative Time on Task 2: 30 hours
Due: 31/05/2026
Weighting: 30%
Groupwork/Individual: Individual
Short extension 3: No
AI assisted?:

You will apply Bayesian methods to real data and deliver a 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: Formal examination period
Weighting: 50%
Groupwork/Individual: Individual
Short extension 3: No
AI assisted?:

You will sit a formal examination assessing 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.

3 An automatic short extension is available for some assessments. Apply through the Service Connect Portal.

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.

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. 

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/

Academic Success

Academic Success 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

To enable students more time to focus on learning, understanding and reflecting on the content of our unit, we have revised the assessment structure as follows. There are now only three assessments: a skills assessment, a report and a final exam. Although no marks are associated with attendance, all activities provide you with key content designed to help you understand the content and complete the assessments. Two reports (each worth 15%) have been combined into a single written report worth 20%.

Unit information based on version 2026.03 of the Handbook