Outline

Overview

The great power of the discipline of Statistics is the possibility to make inferences concerning a large population based on optimally learning from increasingly large and complex data. Critical to successful inference is a deep understanding of the theory when the number of samples and the number of observed features is large and require complex statistical methods to be analysed correctly. In this unit you will learn how to integrate concepts from a diverse suite of specialities in mathematics and statistics such as optimisation, functional approximations and complex analysis to make inferences for highly complicated data. In particular, this unit explores advanced topics in statistical methodology examining both theoretical foundations and details of implementation to applications. The unit is made up of distinct modules that may include (but are not restricted to) asymptotic theory for statistics and econometrics, theory and algorithms for statistical learning with big data, and introduction to optimal semiparametric optimality.

Unit details and rules

Academic unit	Mathematics and Statistics Academic Operations
Credit points	6
Prerequisites ?	None
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	Strong background in probability theory and statistical modelling. Please consult with the coordinator for further information
Available to study abroad and exchange students	No

Teaching staff

Coordinator	Linh Nghiem, linh.nghiem@sydney.edu.au

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written work	Homework Multiple homework	12%	Multiple weeks	Vary	AI allowed
Outcomes assessed:
In-person written or creative task	Quiz 1 in class assessment (Module 1 test)	26%	Week 05 Closing date: 04 Sep 2026	50 minutes	AI prohibited
Outcomes assessed:
In-person written or creative task	Quiz 2 In class assessment (Module 2 test)	26%	Week 09 Closing date: 09 Oct 2026	50 minutes	AI prohibited
Outcomes assessed:
Q&A following presentation, submission or placement	Q&A Q&A after Module 3 interactive oral	13%	Week 13 Closing date: 06 Nov 2026	10 minutes	AI prohibited
Outcomes assessed:
Interactive oral	Interactive Oral Module 3 Interactive Oral and Discussion	15%	Week 13 Closing date: 06 Nov 2026	30 minutes	AI prohibited
Outcomes assessed:
Evaluation	Peer Evaluation Contribution and evaluation of the other group鈥檚 interactive oral in Week 13	4%	Week 13	Vary	AI allowed
Outcomes assessed:
Contribution	Tutorial Contribution Attendance and contribution in tutorials	4%	Weekly	50 min per week	AI allowed
Outcomes assessed:
= group assignment ?

Assessment summary

Students will be assessed by both听technical competence and higher-level communication and critical thinking skills.

Homework: 4-5听homework听across semesters, submitted via Canvas. Details will be available on Canvas.
Module 1 and module 2 quizzes: two in-class pen-and-paper individual quizzes in weeks 5 and 9
Contribution to weekly tutorials. Contribution:听There is a contribution mark for each tutorial. There is a total of 12 contribution marks, the best 8 will be counted. Full marks are awarded for 8 out of 12.听听If you miss up to 4 tutorials, they are not eligible for special consideration, as only the top 8 are counted. If you miss more than 4 tutorials (e.g., prolonged illness), then you can apply for special consideration.
Group-based assessments for module 3听in week 13:
- Interactive-oral: students in a group will read several papers for one given topic (chosen by the lecturer) and discuss them with the whole class.听
  听
- Q&A following presentation, submission or placement: evaluate both (1) ability to answer questions at the end from the lecturer/peer after the interactive-oral above and (2) ability to pose meaningful questions to other groups.听
  听
- Peer evaluation: Each student will complete an evaluation form for their own and other group's presentation; the latter also contributes to the "Interactive oral" part of the other groups.

Assessment criteria

The University awards common result grades, set out in the听听(Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

For more information see听.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the听Academic Integrity Policy.

Visit the听Current Students website听for more information on AI in assessments, including听details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website听provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the听Academic Honesty Education Module (AHEM)听which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the听Current Students website听for more information on AI in assessments, including听details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a听simple extension.鈥� The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can鈥檛 complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for听special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students,听听includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy听reflects the University鈥檚 commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Review of stochastic convergence, continuous mapping theorem, etc	Lecture (3 hr)
Week 01	Review of stochastic convergence, continuous mapping theorem, etc	Tutorial (1 hr)
Week 02	Kernel estimation and nonparametric regression	Lecture (3 hr)
Week 02	Kernel estimation and nonparametric regression	Tutorial (1 hr)
Week 03	Kernel estimation and nonparametric regression	Lecture (3 hr)
Week 03	Kernel estimation and nonparametric regression	Tutorial (1 hr)
Week 04	Martingale central limit theorem with applications	Lecture (3 hr)
Week 04	Martingale central limit theorem with applications	Tutorial (1 hr)
Week 05	Quiz 1	Assessment (1 hr)
	Concentration inequalities	Lecture (2 hr)
	Concentration inequalities	Tutorial (1 hr)
Week 06	Concentration inequalities	Lecture (3 hr)
Week 06	Concentration inequalities	Tutorial (1 hr)
Week 07	Complexity of function; uniform law of large numbers, and complexity of function classes	Lecture (3 hr)
Week 07	Complexity of function; uniform law of large numbers, and complexity of function classes	Tutorial (1 hr)
Week 08	M-estimation	Lecture (3 hr)
Week 08	M-estimation	Tutorial (1 hr)
Week 09	Module 2 Quiz	Assessment (1 hr)
	Principles of Statistical Inferences: sufficiency, likelihood, conditionality	Lecture (2 hr)
	Principles of Statistical Inferences: sufficiency, likelihood, conditionality	Tutorial (1 hr)
Week 10	BFF: Bayesian, Frequentist, and Fiducial inference	Lecture (2 hr)
Week 10	BFF: Bayesian, Frequentist, and Fiducial inference	Tutorial (2 hr)
Week 11	BFF: Bayesian, Frequentist and Fiducial inference	Lecture (2 hr)
Week 11	BFF: Bayesian, Frequentist and Fiducial inference	Tutorial (2 hr)
Week 12	Compromises and modern perspectives: Empirical Bayes, penalised/regularizarion, and hybrid inference	Lecture (2 hr)
Week 12	Compromises and modern perspectives: Empirical Bayes, penalised/regularizarion, and hybrid inference	Tutorial (2 hr)
Week 13	Compromises and modern perspectives: Empirical Bayes, penalised/regularizarion, and hybrid inference	Lecture (1 hr)
Week 13	Interactive oral	Assessment (3 hr)

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Demonstrate a coherent and advanced understanding of key concepts in statistical methodology.
LO2. Apply fundamental principles and results in statistics to solve given problems.
LO3. Distinguish and compare the properties of different types of statistical models and statistical methods applicable to them.
LO4. 鈥婭dentify assumptions required for various statistical methods to be valid and devise methods for testing these assumptions.
LO5. 鈥婦evise statistical solutions to complex problems.
LO6. Compose correct proofs of unfamiliar general results in statistical methodology.

Graduate qualities

The graduate qualities are the qualities and skills that all 爆料王 graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one鈥檚 personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

The unit has been updated to reflect new structure of teaching and content.

Additional information

Disclaimer

Important: the 爆料王 regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

To help you understand common terms that we use at the University, we offer an .

爆料王

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Study commitment

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

STAT5610: Advanced Inference

Semester 2, 2026 [Normal day] - Camperdown/Darlington, Sydney

Overview

Unit details and rules

Teaching staff

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Study commitment

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Additional information

Disclaimer

On this page

Useful links