QUT offers a diverse range of student topics for Honours, Masters and PhD study. Search to find a topic that interests you or propose your own research topic to a prospective QUT supervisor. You may also ask a prospective supervisor to help you identify or refine a research topic.
Found 3 matching student topics
Displaying 1–3 of 3 results
Unlocking the Potential of Simplex-Truncated Distributions
This PhD project aims to develop new methods for generating random samples from a specific type of probability distributions called simplex-truncated distributions. These distributions are commonly used in various fields such as statistics, machine learning, and biology.The project will involve the development of new techniques to generate random samples from simplex-truncated distributions. These techniques are based on a method called continuous-time Monte Carlo which is a cutting edge method in statistics that can generate random samples from complex distributions.The main …
- Study level
- Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Continuous time samplers (MCMC at the limit!)
The goal of this project is to develop new continuous time Monte Carlo methods for efficient sampling from high-dimensional distributions. Continuous-time Monte Carlo methods are a class of algorithms that use continuous-time dynamics to generate samples from target distributions, rather than the discrete-time dynamics used in traditional Markov chain Monte Carlo (MCMC) methods. These methods have been shown to have faster mixing and better exploration of the state space, making them particularly appealing samplers for challenging distributions.The main objectives of …
- Study level
- PhD, Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
Big Data ideas for GLMs
The goal of this project is to develop new Bayesian methods for large-scale data analysis using subsampling techniques. The focus of the project will be on generalised linear models (GLMs), which are commonly used models in statistics and machine learning.One of the main challenges in using Bayesian statistics with big data is the high computational cost associated with processing big datasets. The proposed project aims to address this challenge by developing new subsampling techniques for Piecewise Deterministic Markov Process (PDMP) …
- Study level
- Master of Philosophy, Honours
- Faculty
- Faculty of Science
- School
- School of Mathematical Sciences
- Research centre(s)
- Centre for Data Science
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