Scott is Professor and Head of Statistics in the School of Mathematics and Statistics at UNSW. He is internationally recognised for his work in computational and Bayesian statistics, and in particular for developing inferential techniques for computationally intractable models and challenging data. Scott is a recent President of the Statistical Society of Australia and member of the Australian National Committee for Mathematical Sciences. He is currently Chair of the Australiasian Chapter of the International Society of Bayesian Analysis (ISBA) and an ARC Future Fellow. Scott is a previous winner of the Moran Medal (Australian Academy of Science), the G. N. Alexander Medal (Engineers Australia) and the J. G. Russell Award (Australian Academy of Science), and is a previous Australian Research Council Queen Elizabeth II Research Fellow.
Computationally intractable models
Extreme vaule theory
Monte Carlo Methods
Symbolic data analysis
Ph.D. in Statistics, Bristol University, U.K.
M.Sc. in Environmental Statistics and Systems, Lancaster University, U.K.
B.Sc. in Mathematics and Statistics, Lancaster University, U.K.
This research deals with the field of symbolic data analysis. Researchers have developed ways of estimating how components of the underlying data mix and interact to produce the values of the symbols they observe. So the models they build relate more closely to what's really happening.
A fundamental challenge in constructing Big Models is the question of calibration. Big Models typically have a large number of parameters, which need to be inferred from data in a robust and theoretically justifiable way. Approximate Bayesian Computation is one promising approach to tackle this calibration problem for large complex models.
ACEMS researchers have now provided a mathematical treatment that sheds light on the variance reduction properties of the reparameterization trick. The work was presented at the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) in Okinawa, Japan in April 2019, and was subsequently published in the conference proceedings. AISTATS is considered a top conference in Machine Learning.
Rodrigues, G. S., Francis A. R., Sisson S., & Tanaka M. M.
(2018). Inferences on the Acquisition of Multi-Drug Resistance in Mycobacterium Tuberculosis Using Molecular Epidemiological Data. Handbook of Approximate Bayesian Computation. 481-511.
Adams, M. P., Sisson S., O'Brien K. R., Helmstedt K. J., Baker C. M., Koh E. J. Y., et al.
(2020). Propagating uncertainty through to model forecasts: deterministic Lotka-Volterra systems as a case study. British Ecological Society "Quantitative Ecology Virtual Conference".
(2019). New models for symbolic data analysis. 62nd ISI World Statistics Congress.
Adams, M. P., Koh E. J. Y., Vilas M. P., Collier C. J., McKenzie L., Quiroz M., et al.
(2019). Maximising the benefit of ecological data using Bayesian inference for ODEs. Satellite Workshop Applied^2 Probability - “Uncertainty Quantification Applications”.
Clark, S., Sisson S., & Sharma A.
(2020). Tools for enhancing the application of self-organizing maps in water resources research and engineering. Advances in Water Resources - Special issue on Machine Learning. 143, 103676. doi: 10.1016/j.advwatres.2020.103676
Carvajal, G.. E., Roser D.. J., Sisson S., Keegan A.., & Kahn S..
(2017). Bayesian belief network modelling of chlorine disinfection for human pathogenic viruses in municipal wastewater.. Water Research. 109, doi: 10.1016/j.watres.2016.11.008
Li, J., Nott D.J., Fan Y., & Sisson S.
(2017). Extending approximate Bayesian computation methods to high dimensions via a Gaussian copula model. Computational Statistics & Data Analysis. 106, 77-89. doi: 10.1016/j.csda.2016.07.005
Carvajal, G.. E., Branch A.., Sisson S., Roser D.. J., van den Akker B.., Monis P.., et al.
(2017). Virus removal by ultrafiltration: Understanding long-term performance change by application of Bayesian analysis.. Water Research. 122, doi: 10.1016/j.watres.2017.05.057
Lorenz, R., Pitman A. J., & Sisson S.
(2016). Does Amazonian deforestation cause global effects; can we be sure?. Journal of Geophysical Research: Atmospheres. 121(10), 5567-5584. doi: 10.1002/2015JD024357
Garthwaite, P. H., Fan Y., & Sisson S.
(2016). Adaptive optimal scaling of Metropolis-Hastings algorithms using the Robbins-Monro process. Communications in Statistics - Theory and Methods. 45(17), 5098-5111. doi: 10.1080/03610926.2014.936562
Williams, A. N., Mooney S. D., Sisson S., & Marlon J.
(2015). Exploring the relationship between Aboriginal population indices and fire in Australia over the last 20,000years. Palaeogeography, Palaeoclimatology, Palaeoecology. 432, 49-57. doi: 10.1016/j.palaeo.2015.04.030
Carvajal, G., Roser D. J., Sisson S., Keegan A., & Khan S. J.
(2015). Modelling pathogen log10 reduction values achieved by activated sludge treatment using naïve and semi naïve Bayes network models. Water Research. 85, 304-315. doi: 10.1016/j.watres.2015.08.035
Bino, G., Sisson S., Kingsford R. T., Thomas R. F., Bowen S., & Kardol P.
(2015). Developing state and transition models of floodplain vegetation dynamics as a tool for conservation decision-making: a case study of the Macquarie Marshes Ramsar wetland. Journal of Applied Ecology. 52(3), 654-664. doi: 10.1111/1365-2664.12410
Clark, S., Sarlin P., Sharma A., & Sisson S.
(2015). Increasing dependence on foreign water resources? An assessment of trends in global virtual water flows using a self-organizing time map. Ecological Informatics. 26, 192-202. doi: 10.1016/j.ecoinf.2014.05.012
Ryan, L. M., Chen V., Beavan A., Speilmann J., Sisson S., & Kohn R.
(2019). A longitudinal analysis of the developmental trajectories of domain specific and domain generic abilities in high-level football players.
Chin, V.., Gunawan D.., Kohn R., & Sisson S.
(2018). Efficient data augmentation for multivariate probit models with panel data: An application to general practitioner decision-making about contraceptives.