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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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
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The genetic architecture of changes in adiposity during adulthood
Preprint
Venkatesh SS. et al, (2023)
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Learning from data with structured missingness
Journal article
Mitra R. et al, (2023), Nature Machine Intelligence, 5, 13 - 23