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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Machine learning analysis plans for randomised controlled trials: detecting treatment effect heterogeneity with strict control of type I error
Journal article
Watson JA. and Holmes CC., (2020), Trials, 21
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A cautionary note on the use of unsupervised machine learning algorithms to characterise malaria parasite population structure from genetic distance matrices.
Journal article
Watson JA. et al, (2020), PLoS genetics, 16
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A FRAMEWORK FOR ADAPTIVE MCMC TARGETING MULTIMODAL DISTRIBUTIONS
Journal article
Pompe E. et al, (2020), ANNALS OF STATISTICS, 48, 2930 - 2952
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Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension
Journal article
Cruz Rivera S. et al, (2020), The Lancet Digital Health, 2, e549 - e560
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Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension
Journal article
Liu X. et al, (2020), The Lancet Digital Health, 2, e537 - e548