MXN442 Modern Statistical Computing Techniques

Unit Outline: Semester 2 2026, Gardens Point, Internal

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:

1. Employ advanced knowledge of the theory of statistical computation and optimisation necessary for advanced applications in economics, scientific, medical, health and engineering problems.
2. Formulate and apply advanced computational approaches applicable to data science.
3. Demonstrate advanced practical ability to conduct data analyses and simulations using various computational platforms such as R and MATLAB.
4. Summarise and explain in written and/or oral form, the motivation, details and results of a statistical simulation.

Content

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.

Feedback on Learning and Assessment

Feedback will be provided by academic staff through summative assessment tasks and through formative feedback during timetabled classes. You will also have opportunities for peer-to-peer learning and self-reflection to support your learning and skills development.

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.

The use of Generative AI tools is prohibited for this assessment. Please see the Assessment page on the canvas site for the unit for any further explanation.

Assessment: Project (applied)

You will be required to solve a complex problem in computational statistics motivated by a realistic application. You will solve the problem by applying modern and advanced techniques in statistical computing to data, which will involve developing the corresponding computer code. You will write a short technical report that describes your solution methodology, including an analysis and interpretation of the results, reporting on the key findings and insights extracted from the data. This authentic assessment closely simulates day-to-day tasks of computational statisticians working in both research and industry.

This assignment is eligible for the 48-hour late submission period and assignment extensions.

The use of Generative AI tools is prohibited for this assessment. Please see the Assessment page on the canvas site for the unit for any further explanation.

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.

Course Learning Outcomes

MS10 Bachelor of Mathematics (Honours)

1. Demonstrate and apply advanced knowledge and skills in mathematical sciences to critically analyse and solve complex problems within the discipline or in cross-disciplinary fields where mathematics underpins innovation
   Relates to: Problem Solving Task, Project (applied)
2. Communicate complex concepts, methods and findings in the mathematical sciences clearly and effectively to a range of audiences including mathematicians, industry professionals and the general public, using a range of academic, professional, and technical formats
   Relates to: Problem Solving Task, Project (applied)
3. Demonstrate autonomy, accountability, ethical scholarship, and effective collaboration for research and continuous learning, consistent with professional practice in the mathematical sciences
   Relates to: Project (applied)