Publications (Computational Statistics and Machine Learning) | Department of Statistics

Publications

Fernandez, T., Rivera, N. and Teh, Y. (2016) “Gaussian processes for survival analysis”, in Advances in Neural Information Processing Systems 29: 30th Annual Conference on Neural Information Processing Systems 2016. Curran Associates, pp. 5021–5029.

Nicholson, G. and Holmes, C. (2016) “A note on statistical repeatability and study design for high-throughput assays”, Statistics in Medicine, 36(5), pp. 790–798.

Rebeschini, P. and Tatikonda, S. (2016) “A New Approach to Laplacian Solvers and Flow Problems”, arXiv.

Filippi, S., Holmes, C. and Nieto Barajas, L. (2016) “Scalable Bayesian nonparametric measures for exploring pairwise dependence via Dirichlet Process Mixtures”, Electronic Journal of Statistics, 10(2), pp. 3338–3354.

Watson, J., Nieto-Barajas, L. and Holmes, C. (2016) “Characterizing variation of nonparametric random probability measures using the Kullback–Leibler divergence”, Statistics, 51(3), pp. 558–571.

Fernández, T. and Teh, Y. (2016) “Posterior Consistency for a Non-parametric Survival Model under a Gaussian Process Prior.”

Elliott, L. and Teh, Y. (2016) “A nonparametric HMM for genetic imputation and coalescent inference”, Electronic Journal of Statistics, 10(2), pp. 3425–3451.

Taylor, A. et al. (2016) “Artemether-lumefantrine and dihydroartemisinin-piperaquine exert inverse selective pressure on Plasmodium Falciparum drug sensitivity-associated haplotypes in Uganda”, Open Forum Infect Dis, 4(1), p. ofw229.

Anastasiou, A. and Reinert, G. (2016) “Bounds for the normal approximation of the maximum likelihood estimator”, Bernoulli, 23(1), pp. 191–218.

Filippi, S. and Holmes, C. (2016) “A Bayesian nonparametric approach to testing for dependence between random variables”, Bayesian Analysis, 12(4), pp. 919–938.