Computational Statistics and Machine Learning (CSML) addresses how to reason from data in the presence of uncertainty using computational algorithms.
There is a critical mass of staff from across the School of Mathematical Sciences working in these broad areas, both in terms of developing novel methodology and algorithms, as well as utilising them in cutting-edge applications such as infectious disease modelling, image and shape analysis, and data-driven mathematical biology.
Areas of particular focus include: Markov Chain Monte Carlo (MCMC), Approximate Bayesian Computation (ABC), Sequential Monte Carlo (SMC), Bayesian non-parametrics, Scientific Computing for Machine Learning, Computational Optimal Transport.
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