Publications

Teh, Y. (2016) “Bayesian Nonparametric Modeling and the Ubiquitous Ewens Sampling Formula”, Statistical Science, 31(1), pp. 34–36.

Vollmer, S., Zygalakis, K. and Teh, Y. (2016) “Exploration of the (non-)asymptotic bias and variance of stochastic gradient Langevin dynamics”, Journal of Machine Learning Research, 17(9), pp. 1–33.

Caron, F. (2016) “Book Review: Nonlinear Time Series: Theory, methods, and applications with R examples, by Randal Douc, Eric Moulines, David S. Stoffer”, Statistics and Computing, 26(1-2), pp. 571–572.

McGree, J. et al. (2016) “Big data and design of experiments”, in Computational and Statistical Methods for Analysing Big Data with Applications, pp. 111–129.

Daly, A. et al. (2015) “Hodgkin–Huxley revisited: reparametrization and identifiability analysis of the classic action potential model with approximate Bayesian methods”, Royal Society Open Science, 2(12), p. Article number: 150499.

Stiphout, R. et al. (2015) “Abstract B1-56: Distinct roles of copy number and loss-of-heterozygosity in predicting prognosis for breast cancer patients”, in Cancer Research. American Association for Cancer Research (AACR), pp. b1–56.

James, L., Orbanz, P. and Teh, Y. (2015) “Scaled subordinators and generalizations of the Indian buffet process.”

Rebeschini, P. and van Handel, R. (2015) “Can local particle filters beat the curse of dimensionality?”, The Annals of Applied Probability, 25(5), pp. 2809–2866.

Rebeschini, P. and Tatikonda, S. (2015) “Locality in Network Optimization”, arXiv.

Hrabě de Angelis, M. et al. (2015) “Analysis of mammalian gene function through broad-based phenotypic screens across a consortium of mouse clinics”, Nature Genetics, 47(9), pp. 969–978.