• Class Number 5569
  • Term Code 3260
  • Class Info
  • Unit Value 6 units
  • Mode of Delivery In Person
    • Dr Tim Molloy
    • Dr Tim Molloy
  • Class Dates
  • Class Start Date 25/07/2022
  • Class End Date 28/10/2022
  • Census Date 31/08/2022
  • Last Date to Enrol 01/08/2022
SELT Survey Results

This advanced graduate course explores Bayesian inference as a core enabling technology for modern, increasingly autonomous, mechatronic systems. The course introduces the conceptual and mathematical underpinnings of Bayesian inference with a particular emphasis on its ability to integrate model-based prior knowledge about dynamical systems with information derived from sensors and perception systems. Algorithms for Bayesian inference commonly employed in mechatronic systems will be examined in detail including Bayesian filters, Bayesian smoothers, and Bayesian sequential detection algorithms. The theory and algorithms of Bayesian inference will be applied to problems in mechatronics such as localisation and mapping for mobile robots, sensor fusion and moving target tracking for autonomous vehicles, and fault detection for industry 4.0+.

Learning Outcomes

Upon successful completion, students will have the knowledge and skills to:

  1. Explain the mathematical foundations of Bayesian inference including the relationship between prior and posterior probabilities, and likelihoods.
  2. Derive recursive forms of the hidden Markov model filter and the Kalman filter, and discuss the impact of model parameters on their performance.
  3. Evaluate and compare Bayesian inference algorithms including their theoretical optimality properties.
  4. Employ factor-graph formulations and numerical methods to solve large-scale Bayesian inference problems.
  5. Design and implement Bayesian inference algorithms for mechatronic applications such as localisation and mapping, sensor fusion, target tracking, and fault detection.

Required Resources

MATLAB and/or Python 3 will be used throughout the course.

ANU students can download MATLAB from: https://matlab.anu.edu.au/.

Python 3 can be downloaded free from https://www.python.org or as part of the Anaconda distribution from https://www.anaconda.com/products/distribution


Students are encouraged to consult a variety of books on inference including:

  • S. Thrun, W. Burgard, and D. Fox, Probabilistic Robotics, MIT Press, 2005.
  • S. Särkkä, Bayesian Filtering and Smoothing, Cambridge University Press, 2013.
  • V. Krishnamurthy, Partially Observed Markov Decision Processes, Cambridge University Press, 2016.

For a background in probability theory and stochastic processes students are strongly encouraged to consult:

  • J. A. Gubner, Probability and Random Processes for Electrical and Computer Engineers, Cambridge University Press, 2006

Whether you are on campus or studying remotely, there are a variety of online platforms you will use to participate in your study program. These could include videos for lectures and other instruction, two-way video conferencing for interactive learning, email and other messaging tools for communication, interactive web apps for formative and collaborative activities, print and/or photo/scan for handwritten work and drawings, and home-based assessment.

ANU outlines recommended student system requirements to ensure you are able to participate fully in your learning. Other information is also available about the various Learning Platforms you may use.

Staff Feedback

Students will be given feedback in the following forms in this course:

  • written comments
  • verbal comments
  • feedback to whole class, groups, individuals, focus group etc

Student Feedback

ANU is committed to the demonstration of educational excellence and regularly seeks feedback from students. Students are encouraged to offer feedback directly to their Course Convener or through their College and Course representatives (if applicable). Feedback can also be provided to Course Conveners and teachers via the Student Experience of Learning & Teaching (SELT) feedback program. SELT surveys are confidential and also provide the Colleges and ANU Executive with opportunities to recognise excellent teaching, and opportunities for improvement.

Class Schedule

Week/Session Summary of Activities Assessment
1 Course Introduction & Review of Probability
2 Philosophy & Tools of Bayesian Inference
3 Partially Observable Stochastic System Models
4 Filtering in Hidden Markov Models
5 Smoothing in Hidden Markov Models
6 Bayesian Detection Problem Set Due
7 Filtering & Smoothing in Linear Systems
8 Filtering & Smoothing in Nonlinear Systems
9 Guest Lecture(s) Problem Set Due
10 Approximate Inference via Sampling
11 Particle Filtering & Smoothing
12 Course Revision Project Due

Tutorial Registration

via MyTimetable

Assessment Summary

Assessment task Value Learning Outcomes
Problem Sets 10 % 2,3,4
Project 40 % 3,4,5
Final Exam 50 % 1,2,3,5

* If the Due Date and Return of Assessment date are blank, see the Assessment Tab for specific Assessment Task details


ANU has educational policies, procedures and guidelines , which are designed to ensure that staff and students are aware of the University’s academic standards, and implement them. Students are expected to have read the Academic Integrity Rule before the commencement of their course. Other key policies and guidelines include:

Assessment Requirements

The ANU is using Turnitin to enhance student citation and referencing techniques, and to assess assignment submissions as a component of the University's approach to managing Academic Integrity. For additional information regarding Turnitin please visit the Academic Skills website. In rare cases where online submission using Turnitin software is not technically possible; or where not using Turnitin software has been justified by the Course Convener and approved by the Associate Dean (Education) on the basis of the teaching model being employed; students shall submit assessment online via ‘Wattle’ outside of Turnitin, or failing that in hard copy, or through a combination of submission methods as approved by the Associate Dean (Education). The submission method is detailed below.

Moderation of Assessment

Marks that are allocated during Semester are to be considered provisional until formalised by the College examiners meeting at the end of each Semester. If appropriate, some moderation of marks might be applied prior to final results being released.

Assessment Task 1

Value: 10 %
Learning Outcomes: 2,3,4

Problem Sets


Two problem sets will be released during semester, each worth 5% of the total course grade. The problem sets will be based on content covered in the lectures and in the tutorials, with some additional research required. Students may collaborate to solve the problem sets but must write (handwrite or type) and submit their own individual solutions.

Due Dates

The first problem set will be due 23:59pm Friday of Week 6.

The second problem set will be due 23:59pm Friday of Week 9.

Late submissions (after the due dates) will not be marked and will incur a mark of 0. All times are Canberra times (AEST or AEDT).


Solutions to each problem set are to be submitted through Turnitin.

Assessment Task 2

Value: 40 %
Learning Outcomes: 3,4,5



Students are to work in groups of up to 3 to:

  1. Identify a (real-world) inference problem related to mechatronics (e.g., an inference problem arising within automation and robotics, wearable technologies, medical devices etc).
  2. Propose and implement (in software) a solution to the identified inference problem.
  3. Demonstrate the practicality and effectiveness of the proposed solution (e.g., using real data or a standard simulation environment).
  4. Write a report in the form of a scientific article (maximum of 6 pages, double column).

Due Dates

The project is due 23:59pm Friday of Week 12 (Canberra time - AEST or AEDT).

Late submissions (after the due date) will not be marked and will incur a mark of 0.


  • The project report should be submitted as a single PDF file through Turnitin.
  • Project code should be submitted as a single ZIP file through Wattle.

Assessment Task 3

Value: 50 %
Learning Outcomes: 1,2,3,5

Final Exam

Final exam worth 50% of the total mark, and is planned to be scheduled during the final examination period.

Academic Integrity

Academic integrity is a core part of the ANU culture as a community of scholars. The University’s students are an integral part of that community. The academic integrity principle commits all students to engage in academic work in ways that are consistent with, and actively support, academic integrity, and to uphold this commitment by behaving honestly, responsibly and ethically, and with respect and fairness, in scholarly practice.

The University expects all staff and students to be familiar with the academic integrity principle, the Academic Integrity Rule 2021, the Policy: Student Academic Integrity and Procedure: Student Academic Integrity, and to uphold high standards of academic integrity to ensure the quality and value of our qualifications.

The Academic Integrity Rule 2021 is a legal document that the University uses to promote academic integrity, and manage breaches of the academic integrity principle. The Policy and Procedure support the Rule by outlining overarching principles, responsibilities and processes. The Academic Integrity Rule 2021 commences on 1 December 2021 and applies to courses commencing on or after that date, as well as to research conduct occurring on or after that date. Prior to this, the Academic Misconduct Rule 2015 applies.


The University commits to assisting all students to understand how to engage in academic work in ways that are consistent with, and actively support academic integrity. All coursework students must complete the online Academic Integrity Module (Epigeum), and Higher Degree Research (HDR) students are required to complete research integrity training. The Academic Integrity website provides information about services available to assist students with their assignments, examinations and other learning activities, as well as understanding and upholding academic integrity.

Online Submission

You will be required to electronically sign a declaration as part of the submission of your assignment. Please keep a copy of the assignment for your records. Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.

Hardcopy Submission

Unless an exemption has been approved by the Associate Dean (Education) submission must be through Turnitin.

Late Submission

Late submission not permitted. If submission of assessment tasks without an extension after the due date is not permitted, a mark of 0 will be awarded.

Referencing Requirements

The Academic Skills website has information to assist you with your writing and assessments. The website includes information about Academic Integrity including referencing requirements for different disciplines. There is also information on Plagiarism and different ways to use source material.

Extensions and Penalties

Extensions and late submission of assessment pieces are covered by the Student Assessment (Coursework) Policy and Procedure. Extensions may be granted for assessment pieces that are not examinations or take-home examinations. If you need an extension, you must request an extension in writing on or before the due date. If you have documented and appropriate medical evidence that demonstrates you were not able to request an extension on or before the due date, you may be able to request it after the due date.

Privacy Notice

The ANU has made a number of third party, online, databases available for students to use. Use of each online database is conditional on student end users first agreeing to the database licensor’s terms of service and/or privacy policy. Students should read these carefully. In some cases student end users will be required to register an account with the database licensor and submit personal information, including their: first name; last name; ANU email address; and other information.
In cases where student end users are asked to submit ‘content’ to a database, such as an assignment or short answers, the database licensor may only use the student’s ‘content’ in accordance with the terms of service – including any (copyright) licence the student grants to the database licensor. Any personal information or content a student submits may be stored by the licensor, potentially offshore, and will be used to process the database service in accordance with the licensors terms of service and/or privacy policy.
If any student chooses not to agree to the database licensor’s terms of service or privacy policy, the student will not be able to access and use the database. In these circumstances students should contact their lecturer to enquire about alternative arrangements that are available.

Distribution of grades policy

Academic Quality Assurance Committee monitors the performance of students, including attrition, further study and employment rates and grade distribution, and College reports on quality assurance processes for assessment activities, including alignment with national and international disciplinary and interdisciplinary standards, as well as qualification type learning outcomes.

Since first semester 1994, ANU uses a grading scale for all courses. This grading scale is used by all academic areas of the University.

Support for students

The University offers students support through several different services. You may contact the services listed below directly or seek advice from your Course Convener, Student Administrators, or your College and Course representatives (if applicable).

Dr Tim Molloy

Research Interests

Inverse problems in optimal control and dynamic game theory, and information-theoretic probabilistic inference and decision-making for robots and autonomous systems.

Dr Tim Molloy

By Appointment
Dr Tim Molloy

Research Interests

Dr Tim Molloy

By Appointment

Responsible Officer: Registrar, Student Administration / Page Contact: Website Administrator / Frequently Asked Questions