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

Discipline Statistics

Student Contribution Band HECS Band 2 10cp

Level Postgraduate Coursework Level 7 subject

Pre-requisite(s) MATH 7012 AND
MATH 7016

Co-requisite(s) COMP 7006


Students must be enrolled in a postgraduate program.

Learning Outcomes

On successful completion of this subject, students should be able to:

  1. Describe the axioms of probability and the principle of maximum likelihood.
  2. Use density estimation to model continuous data.
  3. Apply the EM algorithm (Expectation-Maximisation Algorithm) to maximise complex likelihood functions.
  4. Evaluate models using computational techniques
  5. Analyse data using Bayesian statistical models and MCMC (Markov-Chain Monte Carlo)

Subject Content

1. Review of Probability Theory and Likelihood
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.

Item Length Percent Threshold Individual/Group Task
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

Teaching Periods


Parramatta - Victoria Rd


Subject Contact Laurence Park Opens in new window

View timetable Opens in new window