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DTSC71-307: Advanced Statistical Learning Models


This subject extends the investigation of modern statistical modelling techniques initiated in Statistical Learning and Regression Models. Topics include models for correlated data including spatial and mixed-effects models, as well as Bayesian hierarchical models including discussion of Markov chain Monte Carlo (MCMC) techniques for calculating posterior estimates, and modern applied re-sampling methods for developing robust measures of model accuracy.  The programming language R will be used in this subject.

Subject details

FacultyBond Business School
Semesters offered
  • September 2022 [Standard Offering]
Study areas
  • Business and Commerce
Subject fees
  • Commencing in May 2020: $4,930

Learning outcomes

1. Demonstrate knowledge of the statistical issues associated with correlated data and random effects.
2. Evaluate and choose between a variety of advanced statistical model structures.
3. Diagnose key aspects of data structures to assess the need for correlational(?) or hierarchical structure.
4. Apply the Bayesian modelling framework, including the use of Markov chain Monte Carlo techniques to find posterior estimates.
5. Apply resampling methods to develop robust measures of model accuracy.
6. Explain the meaning and importance of the results of statistical models with random effects and hierarchical structure.

Enrolment requirements

Requisites: ?

Pre-requisites: ?

Co-requisites: ?

There are no co-requisites.

Restrictions: ?


Subject dates

Future offerings not yet planned.