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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Correction to: Graphing and reporting heterogeneous treatment effects through reference classes.
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, e1009037 - e1009037
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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