Novel methodology advancing applied Bayesian statistics and applications. Bayesian statistical inference has become the dominant statistical method in significant areas of application. The project aims to develop and apply novel Bayesian computational algorithms. Outcomes will advance scientific understanding in significant multi-disciplinary areas such as infectious diseases, neurological disease and human behaviour.
Robust inferences for analysis of longitudinal data. This project will develop novel statistical tools. Outcomes of this project will enable more reliable data analysis and more cost effective designs in environmental and biological studies.
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
Doing Bayesian Statistics Better: an Inter-Disciplinary Perspective for Improving Models, Priors, Design and Applications. Through improving methods for data analysis and design, this project increases the capability of individuals, communities and governments to make correct decisions based on data, leading to immeasurable human, social and financial benefits. It will also directly enhance Australia's international research reputation, promote inter-disciplinary links, promote research by wome ....Doing Bayesian Statistics Better: an Inter-Disciplinary Perspective for Improving Models, Priors, Design and Applications. Through improving methods for data analysis and design, this project increases the capability of individuals, communities and governments to make correct decisions based on data, leading to immeasurable human, social and financial benefits. It will also directly enhance Australia's international research reputation, promote inter-disciplinary links, promote research by women in a non-traditional area, keep intellectual property within Australia, train quality undergraduates and postgraduates, and contribute to public good through its focus on applications in key national priorities: health, environment and genetics. 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
New Bayesian methodology for understanding complex systems using hidden Markov models and expert opinion, environmental, robotics and genomics applications. This project aims to merge four areas of intense international interest in describing complex systems: hidden Markov models and mixtures, semi-parametric and nonparametric approaches, true combination of expert opinion with data, and new Bayesian computational methods based on perfect sampling and particle sampling. The project will signific ....New Bayesian methodology for understanding complex systems using hidden Markov models and expert opinion, environmental, robotics and genomics applications. This project aims to merge four areas of intense international interest in describing complex systems: hidden Markov models and mixtures, semi-parametric and nonparametric approaches, true combination of expert opinion with data, and new Bayesian computational methods based on perfect sampling and particle sampling. The project will significantly contribute to statistical methodology and its ability to inform about real-world problems. A strong focus on applications to genomics, robotics and environmental modelling will bring immediate research and monetary benefit for industry. Expected outcomes include enhanced cross-disciplinary and international linkages, publications, industry-funded projects and highly trained graduates.Read moreRead less
New Directions in Bayesian Statistics: formulation, computation and application to exemplar challenges. Bayesian statistics is a fundamental statistical and machine learning approach for density estimation, data analysis and inference. However, there remain open questions regarding the formulation of the model, the likelihood and priors, and efficient computation. This project proposes new approaches that address these issues, and applies them to two exemplar challenges: the impact of climate ch ....New Directions in Bayesian Statistics: formulation, computation and application to exemplar challenges. Bayesian statistics is a fundamental statistical and machine learning approach for density estimation, data analysis and inference. However, there remain open questions regarding the formulation of the model, the likelihood and priors, and efficient computation. This project proposes new approaches that address these issues, and applies them to two exemplar challenges: the impact of climate change on the Great Barrier Reef and better understanding neurological diseases related aging, in particular Parkinson's Disease. Read moreRead less
Advanced Mixture Models for the Analysis of Modern-Day Data. Extracting key information from huge data sets is critical to the scientific successes of the future. This project will develop novel mixture models that can be used directly to analyse complex and high-dimensional data sets that may consist of thousands of variables observed on only a limited number of entities. In order to handle the challenging problems arising in the latter situation. This project develops mixtures of factor models ....Advanced Mixture Models for the Analysis of Modern-Day Data. Extracting key information from huge data sets is critical to the scientific successes of the future. This project will develop novel mixture models that can be used directly to analyse complex and high-dimensional data sets that may consist of thousands of variables observed on only a limited number of entities. In order to handle the challenging problems arising in the latter situation. This project develops mixtures of factor models with options for skew distributions that can be used to effectively analyse such data. Key applications include the domains of bioinformatics, biostatistics, business, data mining, economics, finance, image analysis, marketing, and personalised medicine, among many others.Read moreRead less
Joint clustering and matching of multivariate samples across objects. The project will provide a novel and very effective approach to the clustering of multivariate samples on objects, say patients, that automatically matches the sample clusters across the objects. A key application is the matching of biologically relevant cell subtypes across patients for use in the study and the clinical diagnosis and prognosis of cancer.
The improvement of climate change investigations by developing and applying innovative evolutionary subset time series modelling using semi-parametric sparse-patterned approaches. With an estimated US$6.98 trillion loss indicated in the Stern review, severe climate change will make world climate conditions harsher and more likely include large natural climate disasters. The health of the Australian economy is critically dependent on decisions of environmental managers. However, most problems of ....The improvement of climate change investigations by developing and applying innovative evolutionary subset time series modelling using semi-parametric sparse-patterned approaches. With an estimated US$6.98 trillion loss indicated in the Stern review, severe climate change will make world climate conditions harsher and more likely include large natural climate disasters. The health of the Australian economy is critically dependent on decisions of environmental managers. However, most problems of complexity arising in climate change involve issues on which we do not possess a deep understanding. This project draws upon a set of inter-disciplinary concepts and models centred in neural networks that enable us to advance our understanding of complexity, leading to superior quantitative tools and models to allow for improved environmental decision-making.
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