Papers
Below is a list of selected papers on some topics that I'm interested in. A full list can be seen on Google Scholar.
Monte Carlo variance reduction
South, L. F. and Sutton, M. (2024). Control variates for MCMC. To appear in Handbook of MCMC. (free online text)
South, L. F., Karvonen, T., Nemeth, C., Girolami, M., & Oates, C. (2022). Semi-Exact Control Functionals From Sard's Method. Biometrika, 10(2), 351-367. (free online text)
South, L. F., Oates, C. J., Mira, A., & Drovandi, C. (2023). Regularised zero-variance control variates for high-dimensional variance reduction. Bayesian Analysis, 14(3), 753-776. (free online text)
South, L. F., Riabiz, M., Teymur, O. & Oates, C. J. (2022). Postprocessing of MCMC. Annual Review of Statistics and Its Application, 9, 529-555. (free online text)
Sequential Monte Carlo
South, L. F., Pettitt, A. N., & Drovandi, C. C. (2019). Sequential Monte Carlo samplers with independent Markov chain Monte Carlo proposals. Bayesian Analysis, 14(3), 753-776. (free online text)
Salomone, R., South, L. F., Drovandi, C. C., & Kroese, D. P. (2018). Unbiased and consistent nested sampling via sequential Monte Carlo. arXiv preprint.
Botha, I., Kohn, R., South, L. F. & Drovandi, C. (2023). Automatically adapting the number of state particles in SMC^2. Statistics and Computing, 33(4), 82. (free online text)
Approximate Bayesian computation
Price, L. F., Drovandi, C. C., Lee, A., & Nott, D. J. (2018). Bayesian synthetic likelihood. Journal of Computational and Graphical Statistics, 27(1), 1-11. (free online text)
An, Z., South, L. F., Nott, D. J., & Drovandi, C. C. (2019). Accelerating Bayesian synthetic likelihood with the graphical lasso. Journal of Computational and Graphical Statistics, 28(2), 471-475. (free online text)
An, Z., South, L. F., & Drovandi, C. (2022). BSL: An R package for efficient parameter estimation for simulation-based models via Bayesian synthetic likelihood. To appear in Journal of Statistical Software, 101(11), 1-33. (free online text)