i-like publications:

1. Stefano Peluchetti, Gareth Roberts, and Bruno Casella.  2013.  The strong weak convergence of the quasi-EA. Queueing Systems, 73(4):447–460.​
2. Krzysztof Latuszynski and Gareth Roberts.  2013.  CLTs and asymptotic variance of time-sampled Markov chains. Methodology and Computing in Applied Probability, 15(1):237–247.
3. Simon Cotter, Gareth Roberts, Andrew Stuart, and David White.  2013.  MCMC methods for functions: modifying old algorithms to make them faster. Statistical Science, 28(3):424–446.
4. Gareth Roberts and Jeffrey Rosenthal.  2013.  A note on formal constructions of sequential conditional couplings. Statistics & Probability Letters, 83(9):2073–2076.
5. Omiros Papaspiliopoulos, Gareth Roberts, and Osnat Stramer.  2013.  Data augmentation for diffusions. Journal of Computational and Graphical Statistics, 22(3):665–688.
6. Giorgos Sermaidis, Omiros Papaspiliopoulos, Gareth Roberts, Alexandros Beskos, and Paul Fearnhead.  2013.  Markov chain Monte Carlo for exact inference for diffusions. Scandinavian Journal of Statistics, 40(2):294–321.
7. Krzysztof Latuszynski, Gareth Roberts, and Jeffrey Rosenthal.  2013.  Adaptive Gibbs samplers and related MCMC methods. The Annals of Applied Probability, 23(1):66–98.
8. Alexandros Beskos, Natesh Pillai, Gareth Roberts, Jesus-Maria Sanz-Serna, and Andrew Stuart.  2013.  Optimal tuning of the hybrid Monte Carlo algorithm. Bernoulli, 19(5A):1501–1534.
9. Fl´avio Goncalves and Gareth Roberts.  2014.  Exact simulation problems for jump-diffusions. Methodology and Computing in Applied Probability, 16(4):907–930.
10. Gareth Roberts and Jeffrey Rosenthal.  2014.  Minimising MCMC variance via diffusion limits, with an application to simulated tempering. The Annals of Applied Probability, 24(1):131–149.
11. Galin Jones, Gareth Roberts, and Jeffrey Rosenthal.  2014.  Convergence of conditional Metropolis-Hastings samplers. Advances in Applied Probability, 46(2):422–445.
12. Chris Sherlock, Alex Thiery, Gareth Roberts, and Jeffrey Rosenthal.  2014.  On the efficiency of pseudo-marginal random walk Metropolis algorithms. Annals of Statistics, 43(1):238–275.
13. Murray Pollock, Adam Johansen, and Gareth Roberts.  2014.  On the exact and epsilon-strong simulation of (jump) diffusions. Bernoulli, 22(2):794-856.
14. ​Sergios Agapiou, Gareth Roberts, and Sebastian Vollmer.  2014.  Unbiased Monte Carlo: posterior estimation for intractable/infinite-dimensional models. arXiv:1411.7713.
15. Louis Aslett, Pedro Esperança and Chris Holmes.  2015.  Encrypted statistical machine learning: new privacy preserving methods. arXiv:1508.06845.
16. Louis Aslett, Pedro Esperança and Chris Holmes.  2015.  A review of homomorphic encryption and software tools for encrypted statistical machine learning, Technical report, University of Oxford. arXiv:1508.06574.
17. Helen Ogden.  2015.  A sequential reduction method for inference in generalized linear mixed models. Electronic Journal of Statistics, 9(1):135-152.
18. Wentao Li and Paul Fearnhead.  2015.  On the asymptotic efficiency of ABC estimators. arXiv:1506.03481.
19. Radu Craiu, Lawrence Gray, Krzysztof Latuszynski, Neal Madras, Gareth Roberts, and Jeffrey Rosenthal.  2015.  Stability of adversarial Markov chains, with an application to adaptive MCMC algorithms. Annals of Applied Probability, 25(6):3592-3623
20. Murray Pollock, Adam Johansen, Krzysztof Latuszynski, and Gareth Roberts.  2015.  Discussion of "Sequential quasi-Monte-Carlo sampling" by Mathieu Gerber and Nicholas Chopin. Journal of the Royal Statistical Society, B.
21. Gareth Roberts and Jeffrey Rosenthal.  2015.  Complexity bounds for MCMC via diffusion limits. Journal of Applied Probability, to appear.
22. Alain Durmus, Gareth Roberts, Gilles Vilmart, and Konstantinos C. Zygalakis.  2015.  Fast Langevin based algorithm for MCMC in high dimensions. arXiv:1507.02166.
23. Felipe Medina-Aguayo, Anthony Lee, and Gareth Roberts.  2015.  Stability of noisy Metropolis-Hastings. arXiv:1503.07066, to appear in Statistics and Computing.
24. Omiros Papaspiliopoulos, Gareth Roberts, and Kasia Taylor.  2015.  Exact sampling of diffusions with a discontinuity in the drift. arXiv: 1511.04112.
25. Paul Fearnhead and Loukia Meligkotsidou.  2015.  Augmentation schemes for particle MCMC. Statistics and Computing, 26(6):1293-1306.
26. Paul Fearnhead, Shoukai Yu, Patrick Biggs, Barbara Holland, Nigel French.  2015.  Estimating the relative rate of recombination to mutation in bacteria from single-locus variants using composite likelihood. Annals of Applied Statistics, 9(1):200-224.
27. Paul Jenkins, Paul Fearnhead and Yun Song.  2015.  Tractable diffusion and coalescence processes for weakly correlated loci. Electronic Journal of Probability, 20, article 58.
28. Chris Nemeth, Paul Fearnhead and Lyudmila Mihaylova.  2015.  Particle approximation of the score and observed information matrix for parameter estimation in state space models with linear computational cost. To appear in Journal of Computational and Graphical Statistics.
29. Helen Ogden.  A caveat on the robustness of composite likelihood estimators: the case of mis-specified random effect distribution.  2016.  Statistica Sinica 26:639-651.
30. Chris Nemeth, Chris Sherlock and Paul Fearnhead.  2016.  Particle Metropolis adjusted Langevin algorithms.  Biometrika, 103(3):701-717.
31. Louis Aslett.  2016.  Cryptographically secure multiparty evaluation of system reliability. arXiv:1604.05180 
32. Robert Maidstone, Toby Hocking, Guillem Rigaill and Paul Fearnhead.  2016.  On optimal multiple changepoints algorithms for big data. To appear in Statistics and Computing.
33. Lawrence Bardwell and Paul Fearnhead.  2016.  Bayesian detection of abnormal segments in multiple time series. To appear in Bayesian Analysis.
34. ​Cristiano Varin, Manuela Cattelan and David Firth.  2016.  Statistical modelling of citation exchange between statistics journals (with discussion). Journal of the Royal Statistical Society A, 179(1):1-63.
35. Helen Ogden. 2016.  On asymptotic validity of approximate likelihood inference. arxiv:1601.07911.
36. Wentao Li and Paul Fearnhead.  2016.  Improved convergence of regression adjusted approximate Bayesian computation. arXiv:1609.07135.
37. Paul Fearnhead, Joris Bierkens, Murray Pollock, Gareth Roberts. 2016.  Piecewise Deterministic Markov Processes for Continuous-Time Monte Carlo. arxiv:1611.07873.
38. Murray Pollock, Paul Fearnhead, Adam M. Johansen, Gareth Roberts. 2016.  The Scalable Langevin Exact Algorithm: Bayesian Inference for Big Data. arxiv:1609.03436.
39. Gero Walter, Louis Aslett & Frank Coolen.  2017.  Bayesian nonparametric system reliability using sets of priors. International Journal of Approximate Reasoning, 80, 67-88.
40. Pedro Esperança, Louis Aslett and Chris Holmes.  2017.  Encrypted accelerated least squares regression.  AISTATS.
41. Christopher Drovandi, Matt Moores and Richard Boys. 2017. Accelerating pseudo-marginal MCMC using Gaussian processes. Computational Statistics & Data Analysis​ 118: 1-17.
42. Sam Livingstone, Michael Faulkner and Gareth Roberts. 2017. Kinetic energy choice in Hamiltonian/hybrid Monte Carlo. arXiv:1706.02649
43. Matt Moores. 2017. Discussion of "Beyond subjective and objective in statistics" by Andrew Gelman and Christian Hennig. Journal of the Royal Statistical Society, Series A.
44. Matt Moores and David Firth. 2017. Discussion of "Sparse graphs using exchangeable random measures" by François Caron and Emily Fox. Journal of the Royal Statistical Society, Series B.

related Background:

Pseudo marginal computations and particle MCMC.

• Andrieu, C. and Roberts, G. O. The pseudo-marginal approach for efficient Monte Carlo computations. Annals of Statistics 37, 697–725 (2009). [Preprint Version]
• Andrieu, C., Doucet, A., and Holenstein, R. Particle Markov chain Monte Carlo (with Discussion). Journal of the Royal Statistical Society, Series B 62, 269–342 (2010).

Likelihood-Free Method

• Marjoram, P., Molitor, J., Plagnol, V., and Tavare, S. Markov chain Monte Carlo without likelihoods. PNAS 100, 15324–15328 (2003).
• Fearnhead, P. and Prangle D. Constructing summary statistics for approximate Bayesian computation: semi-automatic approximate Bayesian computation. Journal of the Royal Statistical Society Series B 74, 419-474 (2012).

Composite and Pseudo Likelihood

• Varin, C., Reid, N., and Firth D. An overview of composite likelihood methods. Statistica Sinica, 21, 5-42 (2011). [Preprint Version]

Simulation and Inference for Intractable Models

• Papaspiliopoulos, O. and Roberts, G. O. Retrospective Markov chain Monte Carlo methods for Dirichlet process hierarchical models. Biometrika 95, 169–186 (2008).
• Beskos, A., and Roberts, G. O. Exact simulation of diffusions. Annals of Applied Probability 15, 2422-2444 (2005)
• Beskos, A., Papaspiliopoulos, O., Roberts, G. O., and Fearnhead, P. Exact and computationally efficient likelihood-based estimation for discretely observed diffusion processes (with discussion). Journal of the Royal Statistical Society Series B 68, 333–382 (2006).
• Keane, M. S. and O’Brien, G. L. A Bernoulli factory. Computer Simulation 4, 213–219 (1994).
• Latuszynski, K., Kosmidis, I., Papaspiliopoulos, O., and Roberts, G. Simulating Events of Unknown Probabilities via Reverse Time Martingales. Random Structures and Algorithms 38(4), 442–453 (2011).

Adaptive Monte Carlo

• Andrieu, C. and Thoms, J. A tutorial on adaptive MCMC. Statistics and Computing 18, 343–373 (2008).

Modern many-core computer architecture.

• Suchard, M., Wang, Q., Chan, C., Frelinger, J., Cron, A., and West, M. Understanding GPU programming for statistical
  computation: Studies in massively parallel massive mixtures. Journal of Computational and Graphical Statistics 19(2),
  419–438 (2010).
• Lee, A., Yau, C., Giles, M., Doucet, A., and Holmes, C. On the utility of graphics cards to perform massively parallel
  simulation of advanced Monte Carlo methods. Journal of Computational and Graphical Statistics 19(4), 769–789
  (2010).