# Dr Anthony Lee

### Assistant Professor

Course Director, BSc Data Science degree.

Member, Warwick Data Science Institute.

Associate Member, Oxford-Man Institute of Quantitative Finance.

Associate Editor, Statistics and Computing.

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.

### News

If you are interested in links between machine learning and computational statistics in the UK, please check out the Network on Computational Statistics and Machine Learning. The next workshop, Autonomous Citizens: Algorithms for Tomorrow's Society, is being held at the University of Warwick on September 3rd and 4th, 2015.

### Teaching

I am Course Director for the BSc Data Science degree.

2014/15: ST340: Programming for Data Science with Ben Graham.

2014/15: OxWasp: Stochastic Simulation with Arnaud Doucet.

2013/14: 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

Wednesday: 4.30pm--5.30pm, and

Friday: 4.30pm--5.30pm.

Outside of term, please email me at anthony.lee [at] warwick.ac.uk 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 Thursday, 23rd April or come to my office hours.

### Research Interests

Monte Carlo Methodology

Computational Statistics

Bayesian Inference

### Graduate Students

Felipe Medina Aguayo. Co-supervised with Gareth Roberts.

Pieralberto Guarniero. Co-supervised with Adam Johansen.

### Publications

F. J. Medina-Aguayo, A. Lee, G. O. Roberts. Stability of noisy Metropolis-Hastings.

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.

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. [preprint]

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

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

N. Whiteley, A. Lee & K. Heine. On the role of interaction in sequential Monte Carlo algorithms. Bernoulli, to appear. [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, to appear.

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. JCGS, to appear.

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. WSC, 2012.

B. C. May, N. Korda, A. Lee & D. Leslie. Optimistic Bayesian sampling in contextual-bandit problems. JMLR 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. JRSS 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. JCGS 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. JRSS B 72(3), 2010.

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

### Talks

01/16: MCMSki 5, Lenzerheide.

12/15: CMStatistics 2015, London.

08/15: SMC 2015 Workshop, Paris.

06/15: Bayesian Inference for Big Data 2015, University of Oxford.

05/15: Econometrics Workshops, University of Surrey.

02/15: Machine Learning Seminar, Gatsby Computational Neuroscience Unit.

02/15: Statistics Seminar, Imperial College London.

### Contact Details

Room C0.16

Department of Statistics

University of Warwick

Coventry CV4 7AL

Phone: +44 2476 150042

Fax: +44 2476 524532

anthony.lee [at] warwick.ac.uk

### Education

D. Phil. (2011)

Statistics

University of Oxford

M. Sc. (2008)

Computer Science

University of British Columbia

B. Sc. (2006)

Computer Science

University of British Columbia