MXN442 Modern Statistical Computing Techniques
To view more information for this unit, select Unit Outline from the list below. Please note the teaching period for which the Unit Outline is relevant.
| Unit code: | MXN442 |
|---|---|
| Prerequisite(s): | ((MXN601 or admission into MS10) and Unit Coordinator approval. MXN601 can be studied in the same teaching period as MXN442) OR (admission into IF80). |
| Other requisite(s): | Unit Coordinator approval is required to enrol. |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $578 |
| Domestic tuition unit fee | $3,528 |
| International unit fee | $4,632 |
Unit Outline: Semester 2 2025, Gardens Point, Internal
| Unit code: | MXN442 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | ((MXN601 or admission into MS10) and Unit Coordinator approval. MXN601 can be studied in the same teaching period as MXN442) OR (admission into IF80). |
| Coordinator: | Mahdi Abolghasemi | mahdi.abolghasemi@qut.edu.au |
Overview
This unit is intended to provide you with skills in advanced computational methods and algorithms for handling complex and computationally demanding problems in statistics. Topics will be selected from Monte Carlo methods for estimating quantities of interest under an assumed complex (possibly Bayesian) statistical model, statistical machine learning methods for analysing challenging data, and techniques for optimally selecting the values of controllable variables in order to reduce the expected costs of running a statistical experiment. The unit is designed to complement a research project in statistics and is oriented to enable you to proceed to a variety of workplaces, or to further professional development, or to research.
Learning Outcomes
On successful completion of this unit you will be able to:
- Employ advanced knowledge of the theory of statistical computation and optimisation necessary for advanced applications in economics, scientific, medical, health and engineering problems.
- Formulate and apply advanced computational approaches applicable to data science.
- Demonstrate advanced practical ability to conduct data analyses and simulations using various computational platforms such as R and MATLAB.
- Summarise and explain in written and/or oral form, the motivation, details and results of a statistical simulation.
Content
Content will be selected from the following (or related) topics:
1. Advanced Bayesian Computational Algorithms
2. Monte Carlo Methods
3. Statistical Machine Learning
4. Optimal Experimental Design
Learning Approaches
The teaching and learning approaches will foster both acquisition of new knowledge at an advanced level and development of your skills. The approaches aim to facilitate your individual understanding of the key concepts and issues that are common to statistical computing in extended and complex problems and applications. Strategies also aim to assist you to develop your professional and life-long learning skills within selected realistic contexts that provide demonstration of key issues and methodology. Teaching and learning activities include problems in context and case studies to facilitate your discussion and to provide a learning environment in which you can develop your problem-solving skills.
You are expected to work not only in any lecture/workshop session times allocated, but also in your own private study time. That is, you are expected to consolidate the material presented by working through a wide variety of exercises, problems and online learning activities in your own time.
For more information regarding expected volume of learning for this unit, please consult QUT Manual of Policies and Procedures, Section C/3.1.
Feedback on Learning and Assessment
Assessment
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
You will submit a combination of both long and short answer workbook problems focussing on theory and application of techniques.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Project (applied)
You will take on a genuinely authentic statistical role where you are faced with a real-world dataset that requires statistical modelling in order to extract valuable information to facilitate prediction and decision making. An on-going challenge facing statisticians in the real world is that many statistical models exist, so it is critically important to understand their strengths and weaknesses, and how to formally compare them. This assessment task will provide you with the necessary skill to undertake these tasks with confidence.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Academic Integrity
Academic integrity is a commitment to undertaking academic work and assessment in a manner that is ethical, fair, honest, respectful and accountable.
The Academic Integrity Policy sets out the range of conduct that can be a failure to maintain the standards of academic integrity. This includes, cheating in exams, plagiarism, self-plagiarism, collusion and contract cheating. It also includes providing fraudulent or altered documentation in support of an academic concession application, for example an assignment extension or a deferred exam.
You are encouraged to make use of QUT’s learning support services, resources and tools to assure the academic integrity of your assessment. This includes the use of text matching software that may be available to assist with self-assessing your academic integrity as part of the assessment submission process.
Breaching QUT’s Academic Integrity Policy or engaging in conduct that may defeat or compromise the purpose of assessment can lead to a finding of student misconduct (Code of Conduct – Student) and result in the imposition of penalties under the Management of Student Misconduct Policy, ranging from a grade reduction to exclusion from QUT.
Resources
Risk Assessment Statement
There are no extraordinary risks associated with the classroom/lecture activities in this unit.