Study level

  • PhD
  • Master of Philosophy
  • Honours

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

Dr Leah South
Position
Senior Lecturer in Mathematical Sciences
Division / Faculty
Faculty of Science

Overview

Monte Carlo methods use random sampling to approximate solutions to challenging problems. These methods are helpful for statistical models with many parameters, as discussed in this short video. The methods are particularly useful for Bayesian inference where one wishes to get a rigorous understanding of parameter uncertainty.

Despite having many advantages over their competitors, Monte Carlo methods can be very slow in the context of big data. In this project, you'll help develop scalable Monte Carlo methods to enable timely and reliable insights for big data.

The project is an opportunity to delve into cutting-edge research, gain practical experience in high-performance computing, and contribute to advancements in statistics.

Scholarships

You may be eligible to apply for a research scholarship.

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Keywords

Contact

Contact the supervisor for more information.