Leah South | ARC Centre of Excellence for Mathematical and Statistical Frontiers

Lecturer, Associate Investigator

Queensland University of Technology

I'm a lecturer in statistics at Queensland University of Technology. My research interests are in (Bayesian) computational statistics, especially variance reduction techniques, applications of Stein's method and scalable Monte Carlo methods for big data. Please see my website for more information.

Research Interests:

Approximate Bayesian Computation

Bayesian statistics

Computational statistics

sequential Monte Carlo

Variance reduction

Qualifications:

Bachelor of Mathematics, QUT

Doctor of Philosophy, QUT

Projects

Accelerating Bayesian Synthetic Likelihood With the Graphical Lasso

ACEMS researchers are continuing to build off their research into computationally efficient simulation-based parameter estimation methods for complex models.

Publications

Journal Articles

2022

L. F. South, Riabiz M.., Teymur O.., & Oates C. J. (2022). Postprocessing of MCMC. Annual Review of Statistics and Its Application. 9, doi: 10.1146/annurev-statistics-040220-091727

2021

L. F. South, Karvonen T.., Nemeth C.., Girolami M., & Oates C. J. (2021). Semi-exact control functionals from Sard's method. Biometrika. doi: 10.1093/biomet/asab036

2019

An, Z., L. F. South, 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. doi: 10.1080/10618600.2018.1537928

2018

Salomone, R., L. F. South, Drovandi C. C., & Kroese D. (2018). Unbiased and Consistent Nested Sampling via Sequential Monte Carlo. arXiv. arXiv:1810.12499.

L. F. South, Drovandi C. C., Lee A.., & Nott D.. J. (2018). Bayesian Synthetic Likelihood. Journal of Computational and Graphical Statistics. 27(1), 1-11. doi: 10.1080/10618600.2017.1302882

L. F. South, Mira A., & Drovandi C. C. (2018). Regularised Zero-Variance Control Variates. arXiv. arXiv:1811.05073v1.

2017

L. F. South, Drovandi C. C., & Pettitt A.N. (2017). Discussion of: A Bayesian information criterion for singular models. Journal of the Royal Statistical Society, Series B (Statistical Methodology).

Publicly available softwares

2018

L. F. South, & Salomone R. (2018). SMC-NS.

L. F. South (2018). ZVCV.

Technical reports and unrefereed outputs

2017

L. F. South, Pettitt A.N., Friel N., & Drovandi C. C. (2017). Efficient Use of Derivative Information within SMC Methods for Static Bayesian Models.

2016

L. F. South, Drovandi C. C., & Pettitt A.N. (2016). Sequential Monte Carlo for static Bayesian models with independent MCMC proposals.

L. F. South, Drovandi C. C., Lee A., & Nott D. J. (2016). Bayesian Synthetic Likelihood.

Research links

Contact

l1.south@qut.edu.au

Welcome to the ACEMS Reportal

All contributors should enter data on all of the Centre’s reportable key performance indicators (KPIs).

Dr Leah South

Projects

Accelerating Bayesian Synthetic Likelihood With the Graphical Lasso

Publications

2022

2021

2019

2018

2017

2018

2017

2016

Research links

Contact

On social media

Links

ARC Centre of Excellence for Mathematical & Statistical Frontiers

Welcome to the ACEMS Reportal

All contributors should enter data on all of the Centre’s reportable key performance indicators (KPIs).

Dr Leah South

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Links

ARC Centre of Excellence for Mathematical & Statistical Frontiers