David Huk
I am a second-year PhD student in the CDT in Mathematics and Statistics at Warwick. I am jointly supervised by Dr Rito Dutta and Professor Mark Steel. My main research interests are Computational Statistics, multivariate prediction, and dependence.
Currently, I am working on Bayesian parameter-free prediction which bypasses the need for MCMC methods, and a second project developing a likelihood-free inference methodology for mixed effect models and their extensions.
Prior to my PhD studies, I obtained an Integrated Masters in MORSE from the University of Warwick. My dissertation was under the supervision of Dr Rito Dutta on the topic of probabilistic rainfall forecasting using Generalised Neural Models and Spatial Copulas (link), which we improved into a publication over my first year of PhD.
Publications:
- Quasi-Bayes meets Vines, David Huk, Yuanhe Zhang, Mark Steel, Ritabrata Dutta, arXiv preprint (2024)Link opens in a new window
- David Huk, Lorenzo Pacchiardi, Ritabrata Dutta and Mark Steel’s contribution to the Discussion of “Martingale Posterior Distributions” by Fong, Holmes and Walker, Journal of the Royal Statistical Society Series B: Statistical Methodology (2023)Link opens in a new window
- Probabilistic Rainfall Downscaling: Joint Generalized Neural Models with Censored Spatial Gaussian Copula, David Huk, Rilwan A. Adewoyin, Ritabrata Dutta, arXiv preprint (2023)Link opens in a new window
Talks and Presentations
- Quasi-Bayes meets Vines, Algorithms & Computationally Intensive Inference seminar, University of Warwick, invited talk, (June 2024)
- Probabilistic forecasting with censored spatial copulas via scoring rules, CRiSM Workshop on Fusing Simulations with Data Science, University of Warwick, poster (July 2023)
- Probabilistic forecasting with censored spatial copulas via scoring rules, Workshop on Distance-based Methods in Machine Learning, University College London, poster (June 2023)
- Joint Generalized Neural Models and Censored Spatial Copulas for Probabilistic Rainfall Forecasting, European Geosciences Union General Assembly 2023, Vienna, poster (April 2023)
Tutorial Teaching
- ST 404 Applied Statistical Modelling (Term 2, 2023/2024)
- ST 219 Mathematical Statistics Part B (Term 2, 2022/2023)
