Publications

Journal publications

1. Z. Tian, S. Zhou, A. Lee, Y. Shan, B. Detmann. How to identify earth pressures on in-service tunnel linings: A Bayesian learning perspective. Transportation Geotechnics 48, 2024. [arXiv]

2. Z. Tian, A. Lee, S. Zhou. Adaptive tempered reversible jump algorithm for Bayesian curve fitting. Inverse Problems 40, 2024. [arXiv]

3. C. Andrieu, A. Lee, S. Power, A. Q. Wang. Explicit convergence bounds for Metropolis Markov chains: isoperimetry, spectral gaps and profiles. Ann. Appl. Probab. 34(4), 2024. [arXiv] [pdf]

4. J. Phillips, S. Williams, A. Lee, S. Jenkins. Quantifying uncertainty in probabilistic volcanic ash hazard forecasts, with an application to weather pattern based wind field sampling. Bulletin of Volcanology 85, 2023.

5. Z. Tian, S. Zhou, A. Lee, Y. Zhao, Q. Gong. A Bayesian-based approach for inversion of earth pressures on in-service underground structures. Acta Geotechnica 19, 2023. [accepted manuscript]

6. C. Andrieu, A. Lee, S. Power, A. Q. Wang. Comparison of Markov chains via weak Poincaré inequalities with application to pseudo-marginal MCMC. Ann. Statist. 50(6), 2022. [arXiv] [pdf]

7. C. Sherlock, A. Lee. Variance bounding of delayed-acceptance kernels. Methodol. Comput. Appl. Probab. 24, 2022. [arXiv]

8. J. J. Bon, A. Lee, C. Drovandi. Accelerating sequential Monte Carlo with surrogate likelihoods. Stat. Comput. 31, 2021. [arXiv]

9. L. M. Murray, S. S. Singh, A. Lee. Anytime Monte Carlo. Data-Centric Engineering, 2, 2021. [arXiv of an older version]

10. L. J. Rendell, A. M. Johansen, A. Lee, N. Whiteley. Global consensus Monte Carlo. J. Comput. Graph. Statist. 30(2), 2021. [arXiv]

11. A. Lee, S. S. Singh, M. Vihola. Coupled conditional backward sampling particle filter. Ann. Statist. 48(5), 2020. [arXiv] [code]

12. A. Lee, S. Tiberi, G. Zanella. Unbiased approximations of products of expectations. Biometrika 106(3), 2019. [arXiv]

13. M. Banterle, C. Grazian, A. Lee, C. P. Robert. Accelerating Metropolis-Hastings algorithms by delayed acceptance. Foundations of Data Science 1(2), 2019. [arXiv]

14. A. Lee, N. Whiteley. Variance estimation in the particle filter. Supplementary material. Biometrika 105(3), 2018. [arXiv]

15. G. Deligiannidis, A. Lee. Which ergodic averages have finite asymptotic variance? Ann. Appl. Probab. 28(4), 2018. [arXiv] [pdf]

16. L. F. Price, C. C. Drovandi, A. Lee, D. J. Nott. Bayesian synthetic likelihood. J. Comput. Graph. Statist. 27(1), 2018. [preprint]

17. C. Andrieu, A. Lee, M. Vihola. Uniform ergodicity of the iterated conditional SMC and geometric ergodicity of particle Gibbs samplers. Bernoulli 24(2), 2018. [arXiv]

18. C. Sherlock, A. H. Thiery, A. Lee. Pseudo-marginal Metropolis-Hastings using averages of unbiased estimators. Biometrika 104(3), 2017. [arXiv]

19. P. Guarniero, A. M. Johansen, A. Lee. The iterated auxiliary particle filter. J. Amer. Statist. Assoc. 112(520), 2017. [arXiv]

20. F. J. Medina-Aguayo, A. Lee, G. O. Roberts. Stability of noisy Metropolis-Hastings. Stat. Comput. 26(6), 2016.

21. N. Whiteley, A. Lee. Perfect sampling for nonhomogeneous Markov chains and hidden Markov models. Ann. Appl. Probab. 26(5), 2016. [arXiv]

22. A. Lee, N. Whiteley. Forest resampling for distributed sequential Monte Carlo. Stat. Anal. Data Min. 9(4), 2016. [preprint]

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

24. L. M. Murray, A. Lee, P. E. Jacob. Parallel resampling in the particle filter. J. Comput. Graph. Statist. 25(3), 2016. [arXiv]

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

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

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

28. N. Whiteley, A. Lee. Twisted particle filters. Supplementary material. Ann. Statist. 42(1), 2014. [arXiv]

29. P. Del Moral, P. E. Jacob, A. Lee, L. Murray, G. W. Peters. Feynman–Kac particle integration with geometric interacting jumps. Stoch. Anal. Appl. 31(5), 2013 [arXiv].

30. B. C. May, N. Korda, A. Lee, D. Leslie. Optimistic Bayesian sampling in contextual-bandit problems. J. Mach. Learn. Res. 13(Jun) 2012.

31. A. Lee, F. Caron, A. Doucet, C. Holmes. Bayesian sparsity-path-analysis of genetic association signal using generalized t priors. Stat. Appl. Genet. Mol. Biol. 11(2), 2012 [arXiv].

32. 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. J. Comput. Graph. Statist. 19(4), 2010 [arXiv]. Related Website.

Book chapters

1. C. Andrieu, A. Lee, M. Vihola. Theoretical and methodological aspects of MCMC computations with noisy likelihoods. In Handbook of Approximate Bayesian Computation.

2. A. Doucet, A. Lee. Sequential Monte Carlo methods. In Handbook of Graphical Models. [preprint]

Conference publication

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

Discussions

1. P. A. Gilliot, C. Andrieu, A. Lee, S. Liu, and M. Whitehouse. Discussion of From Denoising Diffusions to Denoising Markov Models by J. Benton, Y. Shi, V. De Bortoli, G. Deligiannidis, A. Doucet. 2024.

2. A. Lee, S. Singh, M. Vihola. Discussion of Unbiased Markov chain Monte Carlo methods with couplings by P. E. Jacob,  J. O’Leary & Y. F. Atchadé. J. R. Stat. Soc. Ser. B Stat. Methodol. 82(3), 2020.

3. M. Gerber, A. Lee. Discussion of Unbiased Markov chain Monte Carlo methods with couplings by P. E. Jacob, J. O’Leary & Y. F. Atchadé. J. R. Stat. Soc. Ser. B Stat. Methodol. 82(3), 2020.

4. A. Finke, A. L. Hetland, A. Lee, A. M. Johansen. Discussion of Sequential quasi Monte Carlo by M. Gerber & N. Chopin. J. R. Stat. Soc. Ser. B Stat. Methodol. 77(3), 2015.

5. A. Lee, C. Andrieu, A. Doucet. Discussion of Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation by P. Fearnhead and D. Prangle. J. R. Stat. Soc. Ser. B Stat. Methodol. 74(3), 2012.

6. C. Andrieu, A.Lee, A. Doucet. Discussion of Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation by P. Fearnhead and D. Prangle. J. R. Stat. Soc. Ser. B Stat. Methodol. 74(3), 2012.

7. A. Lee, C. Holmes. Discussion of Particle Markov chain Monte Carlo methods by C. Andrieu, A. Doucet and R. Holenstein. J. R. Stat. Soc. Ser. B Stat. Methodol. 72(3), 2010.

Technical reports and preprints

1. J. Karjalainen, A. Lee, S. S. Singh, M. Vihola. Mixing time of the conditional backward sampling particle filter.

2. C. Andrieu, A. Lee, S. Power, A. Q. Wang. Weak Poincaré Inequalities for Markov chains: theory and applications.

3. J. Karjalainen, A. Lee, S. S. Singh, M. Vihola. On the Forgetting of Particle Filters.

4. C. Andrieu, A. Lee, S. Power, A. Q. Wang. Poincaré inequalities for Markov chains: a meeting with Cheeger, Lyapunov and Metropolis.

5. J. J. Bon, C. Drovandi, A. Lee. Monte Carlo twisting for particle filters.

6. R. Douc, P. E. Jacob, A. Lee, D. Vats. Solving the Poisson equation using coupled Markov chains.

7. C. Andrieu, A. Lee, S. Livingstone. A general perspective on the Metropolis–Hastings kernel.

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

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

Theses

1. A. Lee On auxiliary variables and many-core architectures in computational statistics. D. Phil. Thesis, Oxford, 2011.

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