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
-
Multivariate phenotype analysis enables genome-wide inference of mammalian gene function.
Journal article
Nicholson G. et al, (2022), PLoS biology, 20
-
Interoperability of statistical models in pandemic preparedness: principles and reality.
Journal article
Nicholson G. et al, (2022), Statistical science : a review journal of the Institute of Mathematical Statistics, 37, 183 - 206
-
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
-
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
-
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