Faculty/School

Faculty of Science

School of Mathematical Sciences

Topic status

We're looking for students to study this topic.

Research centre

Centre for Data Science

Supervisors

Professor Chris Drovandi
Position
ARC Future Fellow
Division / Faculty
Faculty of Science

Overview

Bayesian statistics provide a framework for a statistical inference for quantifying the uncertainty of unknowns based on information pre and post data collection.

This information is captured in the posterior distribution, which is a probability distribution over the space of unknowns given the observed data.

The ability to make inferences based on the posterior essentially amounts to efficiently simulating from the posterior distribution, which can generally not be done perfectly in practice.

This task of sampling may be challenging for various reasons:

  • The posterior distribution is irregular (e.g. multi-modal, non-normal and/or complex dependency structures between components).
  • The likelihood function (the probability function of the data given unknowns) of the statistical model of interest may be expensive to compute.
  • The likelihood function is intractable but can be estimated unbiasedly.
  • The likelihood function is completely intractable but simulation from the model is feasible.
  • The model has a large number of parameters
  • There are several competing models of interest.

Research engagement

Your project will develop or apply cutting-edge methods in computational Bayesian statistics.  This will involving learning about advanced statistical methods by reading papers, and implementing them in computer code.

Research activities

Helping with the implementation of methods in computational Bayesian statistics and running simulations.

Outcomes

The aim of the project is to gain a deeper understanding of, or apply to challenging problems, cutting-edge methods in computational Bayesian statistics.

Skills and experience

Ideally you will have a strong interest in statistics with skills in statistical inference and programming.

Start date

18 November, 2024

End date

14 February, 2025

Location

Gardens Point

Additional information

This project can be extended into an Honours/MPhil project if desired.

Keywords

Contact

Chris Drovandi (c.drovandi@qut.edu.au)