Chris Holmes
Professors of Biostatistics in Genomics
I have a broad interest in the theory, methods and applications of statistics and statistical modelling. My background and beliefs lie in Bayesian statistics which provides a unified framework to stochastic modelling and information processing. I am particularly interested in pattern recognition and nonlinear, nonparametric methods.
Recent publications
-
Where Medical Statistics Meets Artificial Intelligence.
Journal article
Hunter DJ. and Holmes C., (2023), The New England journal of medicine, 389, 1211 - 1219
-
Making the invisible visible: what can we do about biased AI in medical devices?
Journal article
Whitehead M. et al, (2023), BMJ (Clinical research ed.), 382
-
Optimal strategies for learning multi-ancestry polygenic scores vary across traits
Journal article
Lehmann B. et al, (2023), Nature Communications, 14
-
Comparison of machine learning techniques in prediction of mortality following cardiac surgery: analysis of over 220 000 patients from a large national database.
Journal article
Sinha S. et al, (2023), European journal of cardio-thoracic surgery : official journal of the European Association for Cardio-thoracic Surgery, 63
-
Statistical inference with exchangeability and martingales.
Journal article
Holmes CC. and Walker SG., (2023), Philos Trans A Math Phys Eng Sci, 381