MATH 7002 Advanced Statistical Methods
Credit Points 10
Legacy Code 301115
Coordinator Laurence Park Opens in new window
Description There has been a significant trend away from simple statistical models for complex and Big Data. Advanced Statistical Methods is a technical unit that looks at computer intensive statistical techniques for modelling complex data. Students will learn about methods including Density Estimation, the Expectation-Maximisation (EM) algorithm, Bayesian, Markovian and Hidden Markov Models, enabling them to apply sophisticated statistical tools in a Data Science setting.
School Computer, Data & Math Sciences
Student Contribution Band HECS Band 2 10cp
Check your HECS Band contribution amount via the Fees page.
Level Postgraduate Coursework Level 7 subject
Co-requisite(s) COMP 7006
Restrictions Students must be enrolled in a postgraduate program.
- Describe the axioms of probability and the principle of maximum likelihood.
- Use density estimation to model continuous data.
- Apply the EM algorithm (Expectation-Maximisation Algorithm) to maximise complex likelihood functions.
- Evaluate models using computational techniques
- Analyse data using Bayesian statistical models and MCMC (Markov-Chain Monte Carlo)
2. Density Estimation
3. Maximum Likelihood and EM algorithm
4. Jack-knife, Bootstrap and Cross-validation
5. Introduction to Bayesian Methods
6. Markovian and Hidden Markov Models
The following table summarises the standard assessment tasks for this subject. Please note this is a guide only. Assessment tasks are regularly updated, where there is a difference your Learning Guide takes precedence.
|Online Quizzes||5 x 30 minutes||20||N||Individual|
|Case Study||2,000 words||40||N||Individual|
|Applied Project||2,000 words||40||N||Individual|
2022 Semester 2
Parramatta - Victoria Rd
Subject Contact Laurence Park Opens in new window
Attendance Requirements 80% attendance rate is imposed in all core subjects’ due to the nature of class activities that are aligned with subject assessments.