Rare Event Simulation with Heavy Tails. The project provides a rigorous way to enhance our understanding of the mechanisms that bring about catastrophic rare events such as urban flooding, electricity shortages and financial bankrupcy. Australia is at the forefront of exciting recent developments in rare event simulation. The advancement of the knowledge in this area will generate a competitive advantage for various sections of the Australian industry, including the areas of industrial reliabili ....Rare Event Simulation with Heavy Tails. The project provides a rigorous way to enhance our understanding of the mechanisms that bring about catastrophic rare events such as urban flooding, electricity shortages and financial bankrupcy. Australia is at the forefront of exciting recent developments in rare event simulation. The advancement of the knowledge in this area will generate a competitive advantage for various sections of the Australian industry, including the areas of industrial reliability, finance and insurance, were accurate simulation techniques are becoming increasingly important.Read moreRead less
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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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
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
Innovating optimal experimental design through Bayesian statistics. This project will advance optimal experimental design with the development and implementation of novel Bayesian computational algorithms. This will lead to conducting more informative, timely and cost-effective experiments described by complex systems. Outcomes will advance scientific understanding in areas such as pharmacology and ecology.
Classification methods for providing personalised and class decisions. This project provides a novel approach to the clustering of multivariate samples on entities in a class that automatically matches the sample clusters across the entities, allowing for inter-sample variation between the samples in a class. The project aims to develop a widely applicable, mixture-model-based framework for the simultaneous clustering of multivariate samples with inter-sample variation in a class and for the mat ....Classification methods for providing personalised and class decisions. This project provides a novel approach to the clustering of multivariate samples on entities in a class that automatically matches the sample clusters across the entities, allowing for inter-sample variation between the samples in a class. The project aims to develop a widely applicable, mixture-model-based framework for the simultaneous clustering of multivariate samples with inter-sample variation in a class and for the matching of the clusters across the entities in the class. The project will use a statistical approach to automatically match the clusters, since the overall mixture model provides a template for the class. It will provide a basis for discriminating between different classes in addition to the identification of atypical data points within a sample and of anomalous samples within a class. Key applications include biological image analysis and the analysis of data in flow cytometry which is one of the fundamental research tools for the life scientist.Read moreRead less
Statistical estimation and approximation of anomalous diffusion. This project investigates diffusion processes with long memory, heavy-tailed distributions and higher-order information. Each of these characteristics has been a subject of extensive current research. These processes arise in important applications with significant social/economic benefits such as heat conduction and fluid flow in porous media, propagation of seismic waves, transport of drug molecules in living tissues. Built on ou ....Statistical estimation and approximation of anomalous diffusion. This project investigates diffusion processes with long memory, heavy-tailed distributions and higher-order information. Each of these characteristics has been a subject of extensive current research. These processes arise in important applications with significant social/economic benefits such as heat conduction and fluid flow in porous media, propagation of seismic waves, transport of drug molecules in living tissues. Built on our recent fundamental developments of fractional generalised random fields and fractional diffusion equations, this project tackles the key problems of statistical estimation, approximation and prediction of diffusion processes with all the above characteristics in a unified framework not provided by other approaches.Read moreRead less
Bayesian Statistical Inference for Implicitly defined Probability Models. Bayesian statistics has recently been used to provide solutions for a large number of hitherto intractable problems in science and technology. The success of Bayesian statistics has mainly been due to the application of so-called Markov chain Monte Carlo computational techniques. We aim to improve these algorithms, by providing fast, simple and efficient computational implementations. We will use the results to give ins ....Bayesian Statistical Inference for Implicitly defined Probability Models. Bayesian statistics has recently been used to provide solutions for a large number of hitherto intractable problems in science and technology. The success of Bayesian statistics has mainly been due to the application of so-called Markov chain Monte Carlo computational techniques. We aim to improve these algorithms, by providing fast, simple and efficient computational implementations. We will use the results to give insight by carefully quantifying and modelling uncertainty for such topics as the transmission rate of infectious diseases, the spatial distribution of plant and animal species, investigating biological theory for the genome of a virus, and changes in human fertility.Read moreRead less
Improving likelihood estimators: theory and applications to analysing productivity and efficiency and forecasting of probability of economic recession. This project aims to improve one of the most popular statistical methods to empower applied researchers with a more reliable analytical tool. This project will develop mathematical theory and use it to analyse patterns of economic growth, productivity and efficiency of countries. This can be used to forecast probability of entering economic reces ....Improving likelihood estimators: theory and applications to analysing productivity and efficiency and forecasting of probability of economic recession. This project aims to improve one of the most popular statistical methods to empower applied researchers with a more reliable analytical tool. This project will develop mathematical theory and use it to analyse patterns of economic growth, productivity and efficiency of countries. This can be used to forecast probability of entering economic recession, with a focus on Australia.Read moreRead less
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.