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Dr Anthony Lee

Course Director, BSc Data Science degree.

Faculty Fellow, Alan Turing Institute.

Strategic Programme Director, Intel--Turing Partnership.

Member, Warwick Data Science Institute.

Associate Editor, Statistics and Computing and Journal of Computational and Graphical Statistics.

I co-organize Algorithms & Computationally Intensive Inference, an informal reading/discussion group.

From 2011--2013, I was a CRiSM Research Fellow at the University of Warwick.


I am Course Director for the BSc Data Science degree.

2015/16: ST912: Statistical Frontiers

2015--: ST220: Introduction to Mathematical Statistics

2014--: OxWasp: Stochastic Simulation with Arnaud Doucet.

2013--2016: ST340: Programming for Data Science with Ben Graham.

2012/13: ST414: Advanced Topics in Statistics.

Office hours

During term these are in C0.16 on

Mondays: 11.30am--12.30pm.

Wednesdays: 11.30am--12.30pm

I am away the week of 20th February. Personal tutees: if you urgently need to see a faculty member, please arrange an appointment to meet with Dr. Adam Johansen.

It is a good idea to let me know by email if you plan to attend a particular office hour.

Outside of term, please email me at anthony.lee [at] to arrange an appointment. If you need to speak to me urgently or are unable to make one of these times for any reason then please email me and I will try to make alternative arrangements.

Personal tutees: please book a time to see me on Monday 9th January, Friday 13th January, Wednesday 18th January, or come to my office hours.

Research Interests

Bayesian inference

Computational statistics

Monte Carlo methodology

Parallel and distributed algorithms

Scalable data analysis

PhD Students

Felipe Medina Aguayo (MASDOC). Co-supervised with Gareth Roberts.
Now a postdoctoral researcher at Reading with Richard Everitt.

Pieralberto Guarniero. Co-supervised with Adam Johansen.

Lewis Rendell. Co-supervised with Adam Johansen.

Lawrence Middleton (OxWaSP, based in Oxford). Co-supervised with Arnaud Doucet.


L. M. Murray, S. Singh, P. E. Jacob, A. Lee. Anytime Monte Carlo.

C. Sherlock, A. H. Thiery, A. Lee. Pseudo-marginal Metropolis-Hastings using averages of unbiased estimators.

G. Deligiannidis, A. Lee. Which ergodic averages have finite asymptotic variance?

L. F. Price, C. C. Drovandi, A. Lee, D. J. Nott. Bayesian synthetic likelihood. Journal of Computational and Graphical Statistics, to appear.

P. Guarniero, A. M. Johansen, A. Lee. The iterated auxiliary particle filter. Journal of the American Statistical Association, to appear. [arXiv 1511.06286]

A. Lee, N. Whiteley. Variance estimation in the particle filter. Supplementary material.

F. J. Medina-Aguayo, A. Lee, G. O. Roberts. Stability of noisy Metropolis-Hastings. Statistics and Computing 26(6), 2016.

M. Banterle, C. Grazian, A. Lee, C. P. Robert. Accelerating Metropolis-Hastings algorithms by delayed acceptance.

N. Whiteley, A. Lee. Perfect sampling for nonhomogeneous Markov chains and hidden Markov models. Annals of Applied Probability 26(5), 2016. [arXiv 1410.4462]

A. Lee, A. Doucet, K. Łatuszyński. Perfect simulation using atomic regeneration with application to Sequential Monte Carlo.

A. Lee, N. Whiteley. Forest resampling for distributed sequential Monte Carlo. Statistical Analysis and Data Mining 9(4), 2016. [preprint]

C. Drovandi, A. N. Pettitt, A. Lee. Bayesian indirect inference using a parametric auxiliary model. Statistical Science 30(1), 2015. [arXiv 1505.03372]

C. Andrieu, A. Lee & M. Vihola. Uniform ergodicity of the iterated conditional SMC and geometric ergodicity of particle Gibbs samplers. Bernoulli, to appear.

N. Whiteley, A. Lee & K. Heine. On the role of interaction in sequential Monte Carlo algorithms. Bernoulli 22(1), 2016. [arXiv 1309.2918]

P. Del Moral, A. Jasra, A. Lee, C. Yau & X. Zhang. The alive particle filter and its use in particle Markov chain Monte Carlo. Stochastic Analysis and Applications 33(6), 2015. [preprint]

A. Lee & K. Łatuszyński. Variance bounding and geometric ergodicity of Markov chain Monte Carlo kernels for approximate Bayesian computation. Supplementary material. Biometrika 101(3), 2014. [arXiv 1210.6703]

N. Whiteley & A. Lee. Twisted particle filters. Supplementary material. Annals of Statistics 42(1), 2014. [arXiv 1210.0220]

L. M. Murray, A. Lee & P. E. Jacob. Parallel resampling in the particle filter.Journal of Computational and Graphical Statistics 25(3), 2016. [arXiv 1301.4019]

P. Del Moral, P. E. Jacob, A. Lee, L. Murray & G. W. Peters. Feynman-Kac particle integration with geometric interacting jumps. Stochastic Analysis and Applications 31(5), 2013 [arXiv 1211.7191].

A. Lee. On the choice of MCMC kernels for approximate Bayesian computation with SMC samplers. Winter Simulation Conference, 2012.

B. C. May, N. Korda, A. Lee & D. Leslie. Optimistic Bayesian sampling in contextual-bandit problems. Journal of Machine Learning Research 13(Jun) 2012.

A. Lee, C. Andrieu & A. Doucet. Two discussions of Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation by P. Fearnhead and D. Prangle. Journal of the Royal Statistical Society B 74(3), 2012.

A. Lee, F. Caron, A. Doucet & C. Holmes. Bayesian sparsity-path-analysis of genetic association signal using generalized t priors. Statistical Applications in Genetics and Molecular Biology 11(2), 2012 [arXiv 1106.0322].

A. Lee, F. Caron, A. Doucet & C. Holmes. A hierarchical Bayesian framework for constructing sparsity-inducing priors.

A. Lee, C. Yau, M. Giles, A. Doucet & C. Holmes. On the utility of graphics cards to perform massively parallel simulation of advanced Monte Carlo methods.Journal of Computational and Graphical Statistics 19(4), 2010 [arXiv 0905.2441]. Related Website.

A. Lee & C. Holmes. Discussion of Particle Markov chain Monte Carlo methods by C. Andrieu, A. Doucet and R. Holenstein.Journal of the Royal Statistical Society B 72(3), 2010.

A. Lee. Towards smooth particle filters for likelihood estimation with multivariate latent variables. M.Sc. Thesis, UBC, 2008.

Seminars / Workshops / Conferences


07/17: Scalable Inference, Isaac Newton Institute, Cambridge.

05/17: Statistics Seminar, University of Bath.

02/17: Validating and expanding approximate Bayesian computation methods, BIRS workshop, Banff.

02/17: HPC and Big Data, London.

01/17: Statistics Seminar, University of York.

01/17: Statistics Seminar, King's College London.


06/16: MCMC and Diffusion Techniques Workshop, Alan Turing Institute.

04/16: SIAM Conference on Uncertainty Quantification, Lausanne.

03/16: Statistics Seminar, University of Bristol.

02/16: Probabilistic Programming Workshop, Alan Turing Institute.

02/16: Probability, stochastic modelling and financial mathematics Seminar, University of Leeds.

01/16: MCMSki 5, Lenzerheide.