Supervisors:
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Working thesis:
Identifying Subgroups Within Existing Paralympic Swimming Classifications Using Mixture Models by RJMCMC and Particle Filter.
Paralympic swimming currently uses 14 classification categories based on athletes' disabilities and swimming styles. This research investigates whether multiple distinct performance groups exist within a single category, as suggested by competition results.泭
Given the elite level of athlete performance, we model swimming speed using the Gumbel distribution for extreme values. The problem is then framed as a mixture model with an unknown number of components. To estimate the number and structure of these groups, we employ a Bayesian framework using Reversible Jump Markov Chain Monte Carlo (RJMCMC), which allows sampling across model spaces of varying dimensionality. Particle filter methods are also explored as a complementary approach for identifying potential subgroups.
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Research interests:
Bayesian Inference, MCMC, Particle Filter
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Academic history:
2013-2017, Bachlor of Science in Mathematics and Applied Mathematics, University of Shanghai for Science and Techonology, China
2017-2018, Master of Science in Applied Mathematics, University of Manchester, UK
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Professional history:
2020-2024, Software engineer, Ericsson Shanghai, China
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