MXN601 Advanced Stochastic Modelling
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: | MXN601 |
|---|---|
| Prerequisite(s): | (MXN501) or (192cps in SV03 or IV04 or MV05 or BV06 or EV08) or (Admission to IV53 or IV54 or IV55 or IV56 or IV58) |
| Credit points: | 12 |
| Timetable | Details in HiQ, if available |
| Availabilities |
|
| CSP student contribution | $555 |
| Domestic tuition unit fee | $3,348 |
| International unit fee | $4,392 |
Unit Outline: Semester 1 2024, Gardens Point, Internal
| Unit code: | MXN601 |
|---|---|
| Credit points: | 12 |
| Pre-requisite: | (MXN501) or (192cps in SV03 or IV04 or MV05 or BV06 or EV08) or (enrolment in IV53 or IV54 or IV55 or IV56 or IV58). |
| Coordinator: | Gentry White | gentry.white@qut.edu.au |
Overview
This unit covers advanced statistical models and methods required for a mathematically trained data scientist. This unit will develop skills in statistical modelling and parameter estimation methods to extract insights from small and large datasets collected from complex systems. Developing an understanding of stochastic processes will provide skills for building statistical models of complex real world processes including areas from communication systems and networks to traffic to law to biology to financial analysis linking with other modern areas of mathematics. This unit introduces advanced statistical inference techniques that are important tools in describing data and developing models. Indeed, such methods are essential when drawing conclusions from a data generation process that is subject to random variability. This unit also provides a statistical basis for further advanced units in statistics.
Learning Outcomes
On successful completion of this unit you will be able to:
- Apply the basic concepts, principles and methods of stochastic and statistical modelling in analysing processes and data and interpreting real problems.
- Interpret non-mathematical information to identify variables, choosing appropriate models and using mathematical tools, to enable investigation and solution of a given problem.
- Use statistical inference and data analysis concepts and skills as part of a problem solving approach to real life problems.
- Apply advanced statistical skills and competencies in a variety of professional circumstances.
Content
Developing skills in using mathematical tools to solve statistical problems. Skills and familiarity in describing and setting up problems, and identifying methods and tools to solve them. Building on earlier studies to develop understanding and ability to work with multivariate distributions, functions of random variables, stochastic models including time varying Markov chains, and Markov processes such as queuing and birth and death processes. Developing familiarity and ability to use and interpret parameters and properties of key distributions, and models of stochastic and statistical processes. Construction of the likelihood function, maximum likelihood estimation and properties of maximum likelihood estimators, likelihood based confidence intervals, and hypothesis testing/model choice. An introduction to the Bayesian statistics paradigm for statistical inference, based on the posterior distribution. Markov chain Monte Carlo methods for sampling from the posterior distribution and Bayesian model choice techniques.
Learning Approaches
This unit involves 2 hours of lectures each week (which may be offered online) where theory and concepts will be presented and discussed, and where you will be exposed to the processes required to solve problems using the methods of this unit. There will also be 2 hours of practical activities.
A combination of discussions, use of purpose-written lecture notes, working through small and larger real world problems, using computer-based materials, and expressing solutions individually and in groups, will promote your creativity in problem-solving, critical assessment skills, and intellectual debate. You will be encouraged to engage in aspects of professionalism and ethics in the practice of Data Science. The standards of the discipline as well as appropriate approaches to the communication of mathematical and statistical information will be conveyed via the examples presented in lectures and workshops.
You are expected to work 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 during class by working a wide variety of exercises, problems and online learning activities in your own time.
Feedback on Learning and Assessment
Formative feedback will be provided for the in-semester assessment items by way of written comments on the assessment items, student perusal of the marked assessment piece and informal interview as required.
Assessment
Overview
Formative feedback will be provided for the in-semester assessment items by way of written comments on the assessment items, student perusal of the marked assessment piece and informal interview as required.
Unit Grading Scheme
7- point scale
Assessment Tasks
Assessment: Problem Solving Task
This assessment item consists of exercises and problems both in and out of class strategically timed to optimise your learning. They cover the core operational knowledge and skills of the unit and provide you with an excellent way of learning through applying techniques to real problems within context. The individual components of the exercises and problems will be marked with feedback and returned to you to assist with understanding and future assessment items.
This assignment is eligible for the 48-hour late submission period and assignment extensions.
Assessment: Examination (invigilated)
Assessment based on and representative of the work covered during the semester.
The examination will require attendance on QUT campus.
Academic Integrity
Students are expected to engage in learning and assessment at QUT with honesty, transparency and fairness. Maintaining academic integrity means upholding these principles and demonstrating valuable professional capabilities based on ethical foundations.
Failure to maintain academic integrity can take many forms. It includes cheating in examinations, plagiarism, self-plagiarism, collusion, and submitting an assessment item completed by another person (e.g. contract cheating). It can also include providing your assessment to another entity, such as to a person or website.
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.
Further details of QUT’s approach to academic integrity are outlined in the Academic integrity policy and the Student Code of Conduct. Breaching QUT’s Academic integrity policy is regarded as student misconduct and can lead to the imposition of penalties ranging from a grade reduction to exclusion from QUT.
Resources
Risk Assessment Statement
There are no out of the ordinary risks associated with this unit, as all classes will be held in ordinary lecture theatres. Emergency exits and assembly areas will be pointed out in the first few lectures. You are referred to the University policy on health and safety. http://www.mopp.qut.edu.au/A/A_09_01.jsp