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.
Spatio-Temporal Statistics and its Application to Remote Sensing. By their very nature, environmental processes involve strong spatial and temporal variability. Inferring cause-effect relationships requires the incorporation of spatial and temporal dependence in the statistical models. The aims of this project are to develop mass-balanced hierarchical spatio-temporal statistical models, new loss functions that are relevant to multivariate processes, and optimal estimators obtained from the hiera ....Spatio-Temporal Statistics and its Application to Remote Sensing. By their very nature, environmental processes involve strong spatial and temporal variability. Inferring cause-effect relationships requires the incorporation of spatial and temporal dependence in the statistical models. The aims of this project are to develop mass-balanced hierarchical spatio-temporal statistical models, new loss functions that are relevant to multivariate processes, and optimal estimators obtained from the hierarchical model's predictive distribution. These methodologies are intended to be applied to the estimation of near-surface fluxes of atmospheric carbon dioxide, using massive remote sensing datasets from satellites and other data sources.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0775510
Funder
Australian Research Council
Funding Amount
$400,000.00
Summary
Australian Social Science Data Archive: Network Extension and Sub-archive Development. The Australian Social Science Data Archive is a national facility that allows all researchers and members of the public to access a wide range of social science data sets for on-line analysis. The archive contains data that covers forty years of social, political and economic surveys. The archive also acts as a gateway for social science researchers to access data from equivalent overseas institutions in North ....Australian Social Science Data Archive: Network Extension and Sub-archive Development. The Australian Social Science Data Archive is a national facility that allows all researchers and members of the public to access a wide range of social science data sets for on-line analysis. The archive contains data that covers forty years of social, political and economic surveys. The archive also acts as a gateway for social science researchers to access data from equivalent overseas institutions in North America, the European Union and OECD countries.Read moreRead less
Industrial Transformation Training Centres - Grant ID: IC190100031
Funder
Australian Research Council
Funding Amount
$3,973,202.00
Summary
ARC Training Centre in Data Analytics for Resources and Environments (DARE). Understanding the cumulative impact of actions regarding the use of our resources has important long-term consequences for Australia’s economic, societal and environmental health. Yet despite the importance of these cumulative impacts, and the availability of data, many decisions and policies are based on limited amounts of data and rudimentary data analysis, with little appreciation of the critical role that understand ....ARC Training Centre in Data Analytics for Resources and Environments (DARE). Understanding the cumulative impact of actions regarding the use of our resources has important long-term consequences for Australia’s economic, societal and environmental health. Yet despite the importance of these cumulative impacts, and the availability of data, many decisions and policies are based on limited amounts of data and rudimentary data analysis, with little appreciation of the critical role that understanding and quantifying uncertainty plays in the process. The aim of Data Analytics in Resources and Environment (DARE) is to develop and deliver the data science skills and tools for Australia’s resource industries to make the best possible evidence-based decisions in exploiting and stewarding the nation’s natural resources.Read moreRead less
Trans-dimensional and Approximate Bayesian Computation. Many applied scientists in Australia, particularly those in the biological, medical and environmental sciences are now interested in incorporating Bayesian statistical methodologies into their research.
The development of more generic and efficient Bayesian statistical methods will not only benefit applied statisticians but also the more occasional users of statistics in other disciplinary areas. The success of this project will enhance Au ....Trans-dimensional and Approximate Bayesian Computation. Many applied scientists in Australia, particularly those in the biological, medical and environmental sciences are now interested in incorporating Bayesian statistical methodologies into their research.
The development of more generic and efficient Bayesian statistical methods will not only benefit applied statisticians but also the more occasional users of statistics in other disciplinary areas. The success of this project will enhance Australia's reputation as a strong contributor to the development of Bayesian methodologies. Two PhD students will also be provided training in computational Bayesian statistics.Read moreRead less
ARC Centre for Complex Dynamic Systems & Control. Complex dynamic systems are an inescapable feature of the world we live in. Modelling, analysing and optimizing complex behaviour is crucial for environment, process industry, biomedical, energy distribution, transportation and other applications. The Centre for Complex Dynamic Systems and Control will become an international authority in the analysis, design and optimization of complex dynamic systems, pursuing both outstanding fundamental and c ....ARC Centre for Complex Dynamic Systems & Control. Complex dynamic systems are an inescapable feature of the world we live in. Modelling, analysing and optimizing complex behaviour is crucial for environment, process industry, biomedical, energy distribution, transportation and other applications. The Centre for Complex Dynamic Systems and Control will become an international authority in the analysis, design and optimization of complex dynamic systems, pursuing both outstanding fundamental and cutting edge applied research outcomes. These outcomes will be of specific benefit to partner organizations including minerals, process, metal forming, and automotive industries.Read moreRead less
Australian Laureate Fellowships - Grant ID: FL150100150
Funder
Australian Research Council
Funding Amount
$2,413,112.00
Summary
Bayesian learning for decision making in the big data era. Bayesian learning for decision making in the big data era: This fellowship project aims to develop new techniques in evidence-based learning and decision-making in the big data era. Big data has arrived, and with it a huge global demand for statistical knowledge and skills to analyse these data for improved learning and decision-making. This project will seek to address this need by creating a step-change in knowledge in Bayesian statist ....Bayesian learning for decision making in the big data era. Bayesian learning for decision making in the big data era: This fellowship project aims to develop new techniques in evidence-based learning and decision-making in the big data era. Big data has arrived, and with it a huge global demand for statistical knowledge and skills to analyse these data for improved learning and decision-making. This project will seek to address this need by creating a step-change in knowledge in Bayesian statistics and translating this knowledge to real-world challenges in industry, environment and health. The new big data statistical analysts trained through the project could also create much needed capacity at national and international levels.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE210101864
Funder
Australian Research Council
Funding Amount
$442,500.00
Summary
Unlocking Urban Airspace for Drone Transport. This project aims to accurately quantify the mid-air collision risk associated with low-altitude unmanned operations in urban airspace through the creation of new data-driven collision risk modelling techniques. Without such techniques, drone operations remain suppressed so their true potential cannot be realised. The collision risk models address this by providing the key missing knowledge that can underpin/enable vital unmanned traffic management ....Unlocking Urban Airspace for Drone Transport. This project aims to accurately quantify the mid-air collision risk associated with low-altitude unmanned operations in urban airspace through the creation of new data-driven collision risk modelling techniques. Without such techniques, drone operations remain suppressed so their true potential cannot be realised. The collision risk models address this by providing the key missing knowledge that can underpin/enable vital unmanned traffic management applications, including airspace design and the development of separation standards. This can ultimately enable greater access to urban airspace without compromising air safety such that we unlock the commercial and societal benefits of drone use and help modernise urban air transportation.
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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.
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