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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Bayesian imputation of COVID-19 positive test counts for nowcasting under reporting lag
Journal article
Jersakova R. et al, (2022), JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES C-APPLIED STATISTICS
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Author Correction: Statistical machine learning of sleep and physical activity phenotypes from sensor data in 96,220 UK Biobank participants.
Journal article
Willetts M. et al, (2022), Scientific reports, 12
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Building an evidence standards framework for artificial intelligence-enabled digital health technologies
Journal article
Unsworth H. et al, (2022), The Lancet Digital Health, 4, e216 - e217
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Time varying association between deprivation, ethnicity and SARS-CoV-2 infections in England: A population-based ecological study.
Journal article
Padellini T. et al, (2022), The Lancet regional health. Europe
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Improving local prevalence estimates of SARS-CoV-2 infections using a causal debiasing framework.
Journal article
Nicholson G. et al, (2022), Nat Microbiol, 7, 97 - 107