Scalable and Robust Bayesian Inference for Implicit Statistical Models. This project aims to develop the next generation of efficient methods for fitting complex simulation-based statistical models to data. Practitioners and scientists are interested in such implicit models to enable discoveries, produce accurate predictions and inform decisions under uncertainty. However, the associated computational cost has restricted researchers to implicit models that must have a small number of parameters ....Scalable and Robust Bayesian Inference for Implicit Statistical Models. This project aims to develop the next generation of efficient methods for fitting complex simulation-based statistical models to data. Practitioners and scientists are interested in such implicit models to enable discoveries, produce accurate predictions and inform decisions under uncertainty. However, the associated computational cost has restricted researchers to implicit models that must have a small number of parameters and be well specified, impeding scientific progress. This project will develop new computational methods and algorithms for implicit models that scale to high dimensions and are robust to misspecification. Benefits will arise from the more routine use of implicit models in epidemiology, biology, ecology and other fields.Read moreRead less
Advances in Sequential Monte Carlo Methods for Complex Bayesian Models. This project aims to develop efficient statistical algorithms for parameter estimation of complex stochastic models that currently cannot be handled. Parameter estimation is an essential component of mathematical modelling for answering scientific questions and revealing new insights. Current parameter estimation methods can be inefficient and require too much user intervention. This project will develop novel Bayesian alg ....Advances in Sequential Monte Carlo Methods for Complex Bayesian Models. This project aims to develop efficient statistical algorithms for parameter estimation of complex stochastic models that currently cannot be handled. Parameter estimation is an essential component of mathematical modelling for answering scientific questions and revealing new insights. Current parameter estimation methods can be inefficient and require too much user intervention. This project will develop novel Bayesian algorithms that are optimally automated and efficient by exploiting ever-improving parallel computing devices. The new methods will allow practitioners to process realistic models, enabling new scientific discoveries in a wide range of disciplines such as biology, ecology, agriculture, hydrology and finance.Read moreRead less
Generalised Degrees of Freedom and Probabilistic Regularisation. This project intends to develop novel statistical tools for more accurate prediction by taking account of model complexity and uncertainties associated with the fitting procedure. The project also plans to develop a novel shrinkage approach via new penalty functions to avoid over-fitting and asymptotic properties. The key applications may include genetic studies where the number of predictors is large and biological experiments whe ....Generalised Degrees of Freedom and Probabilistic Regularisation. This project intends to develop novel statistical tools for more accurate prediction by taking account of model complexity and uncertainties associated with the fitting procedure. The project also plans to develop a novel shrinkage approach via new penalty functions to avoid over-fitting and asymptotic properties. The key applications may include genetic studies where the number of predictors is large and biological experiments where multivariate and temporal data are often collected – for example economical breeding in animal and fish farming and more effectively detecting the genes of interest in genetic studies on human, animals and plants.Read moreRead less
Statistical methods for quantifying variation in spatiotemporal areal data. This project aims to develop new statistical methods for extracting insights into spatial and temporal variation in areal data. These tools will extend the Australian Cancer Atlas which provides small area estimates for 20 cancers across Australia. The project is significant because it will allow government and other organisations to reap dividends from investment in collecting spatial information and it will enable mode ....Statistical methods for quantifying variation in spatiotemporal areal data. This project aims to develop new statistical methods for extracting insights into spatial and temporal variation in areal data. These tools will extend the Australian Cancer Atlas which provides small area estimates for 20 cancers across Australia. The project is significant because it will allow government and other organisations to reap dividends from investment in collecting spatial information and it will enable modelled small-area estimates to be released without compromising confidentiality. The expected outcomes include new statistical knowledge and new insights into cancer. The results will benefit the many disciplines, managers and policy makers that make decisions based on geographic data mapped over space and time. Read moreRead less