Supervisors
- Position
- Senior Lecturer in Mathematical Sciences
- Division / Faculty
- Faculty of Science
- Position
- Division / Faculty
Overview
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 objectives of this project are:
- Develop new methods for generating random samples from simplex-truncated distributions.
- Test the newly developed methods on different examples of simplex-truncated distributions.
- Compare the performance of the new methods with existing methods.
- Explore the potential applications of the new methods in different fields.
Research activities
The student will be working with a joint team of researchers (Matt Sutton and Matthew Adams) and will have the opportunity to publish their findings in academic journals. They will also have the chance to present their research and receive feedback from experts in international conferences and workshops.
Outcomes
By the end of the project the potential outcomes will include:
- Developing a novel approach for efficient sampling .
- Software outputs implementing methods.
- Presenting results suitable to journals in interdisciplinary research or machine learning conferences.
Skills and experience
This project is suitable for students with a background in mathematics, statistics, or computer science and a general interest in the field of probability and statistics. Prior knowledge of MCMC is not required.
Keywords
Contact
Contact the supervisors for more information.