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
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Optimal strategies for learning multi-ancestry polygenic scores vary across traits
Journal article
Lehmann B. et al, (2023), Nature Communications, 14
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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
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Statistical inference with exchangeability and martingales.
Journal article
Holmes CC. and Walker SG., (2023), Philos Trans A Math Phys Eng Sci, 381
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Generating the right evidence at the right time: Principles of a new class of flexible augmented clinical trial designs.
Journal article
Dunger-Baldauf C. et al, (2023), Clin Pharmacol Ther
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A spatio-temporal framework for modelling wastewater concentration during the COVID-19 pandemic.
Journal article
Li G. et al, (2023), Environ Int, 172