Study level

  • Master of Philosophy
  • Honours
  • Vacation research experience scheme

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 Matthew Adams
Position
Senior Lecturer in Mathematical Sciences
Division / Faculty
Faculty of Science
Dr Matt Sutton
Position
Lecturer in Statistical Inference for Complex Models
Division / Faculty
Faculty of Science

Overview

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 objectives of this project are:

  1. Develop new methods for generating random samples from simplex-truncated distributions.
  2. Test the newly developed methods on different examples of simplex-truncated distributions.
  3. Compare the performance of the new methods with existing methods.
  4. 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:

  1. Developing a novel approach for efficient sampling .
  2. Software outputs implementing methods.
  3. 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.