University of Oxford, Department of Statistics logo
University of Oxford, Department of Statistics logo
To Department of Statistics Site

Publications

Pompe, E., Holmes, C. and Łatuszyński, K. (2020) “A framework for adaptive MCMC targeting multimodal distributions”, The Annals of Statistics, 48(5), pp. 2930–2952.
Bozhilova, L. et al. (2020) “COGENT: evaluating the consistency of gene co-expression networks”, Bioinformatics, 37(13), pp. 1928–1929.
Liu, X. et al. (2020) “Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension”, Lancet Digital Health, 2(10), pp. e537 - e548.
Cruz Rivera, S. et al. (2020) “Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension”, Lancet Digital Health, 2(10), pp. e549 - e560.
Elliott, A. et al. (2020) “Core-periphery structure in directed networks”, Proceedings of the Royal Society of London. Series A, Mathematical and Physical Sciences, 476(2241).
Chan, A.-W. et al. (2020) “Reporting guidelines for clinical trial reports for interventions involving artificial intelligence: the CONSORT-AI extension”, Nature Medicine, 26(9), pp. 1364–1374.
Fitzsimons, J. et al. (2020) “A note on blind contact tracing at scale with applications to the COVID-19 pandemic”, in. Association for Computing Machinery (ACM), pp. 1–6.
Xu, J. et al. (2020) “MetaFun: meta-learning with iterative functional updates”, Proceedings of the 37th International Conference on Machine Learning, pp. 10617–10627.
Baddock, H. et al. (2020) “Characterisation of the SARS-CoV-2 ExoN (nsp14ExoN-nsp10) complex: implications for its role in viral genome stability and inhibitor identification.”
Baddock, H. et al. (2020) “Characterisation of the SARS-CoV-2 ExoN (nsp14ExoN-nsp10) complex: implications for its role in viral genome stability and inhibitor identification”, bioRxiv [Preprint].