About the Project: The epistemic uncertainty caused by variable dimensionality and model structures is a widespread problem in various signal processing applications such as nonlinear time series modelling, system identification, communication system channel modelling. One of the most competent ways to solve this problem is to address using the reversible jump Markov chain Monte Carlo (RJMCMC), a robust Bayesian sampling algorithm for trans-dimensional applications. Despite its potential to make inferences for different sizes and classes of model spaces, RJMCMC suffers from technical issues such as efficient proposal design and being computationally expensive.
The optimal transport (OT) is an important mathematical theory, which maps distributions of mass via minimizing a particular integrated transport cost expressing the requirement to rearrange samples. In the last couple of years, the OT methodology has aroused interest in Bayesian statistics especially being accommodated an advanced and optimal way of defining the transition kernels.
This project aims to
· develop strategies to efficiently design proposals via the optimal transform theory, which will be used to design unique proposals.
· develop robust statistical algorithms with convergence acceleration which benefit from trans-dimensional sampling and optimal transport theory.
· demonstrate the superiority of the developed approaches in various signal and image processing applications such as time series modelling and prediction, image segmentation, and channel modelling.
Keywords: Bayesian Sampling, MCMC Methods, Optimal Transport, Time Series analysis, Prediction
For more information about this project, please contact Dr Oktay Karakus: [Email Address Removed].
Academic criteria: A 2:1 Honours undergraduate degree or a master's degree, in computing or a related subject. Applicants with appropriate professional experience are also considered. Degree-level mathematics (or equivalent) is required for research in some project areas.
Applicants for whom English is not their first language must demonstrate proficiency by obtaining an IELTS score of at least 6.5 overall, with a minimum of 6.0 in each skills component.
How to apply:
Please contact the supervisors of the project prior to submitting your application to discuss and develop an individual research proposal that builds on the information provided in this advert. Once you have developed the proposal with support from the supervisors, please submit your application following the instructions provided below
This project is accepting applications all year round, for self-funded candidates via https://www.cardiff.ac.uk/study/postgraduate/research/programmes/programme/computer-science-and-informatics
In order to be considered candidates must submit the following information:
- Supporting statement
- CV
- In the ‘Research Proposal’ section of the application enter the name of the project you are applying to and upload your Individual research proposal, as mentioned above in BOLD
- Qualification certificates and Transcripts
- Proof of Funding. For example, a letter of intent from your sponsor or confirmation of self-funded status (In the funding field of your application, insert Self-Funded)
- References x 2
- Proof of English language (if applicable)
For more information about this project, please contact [Email Address Removed]
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