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.
Discovery Early Career Researcher Award - Grant ID: DE130101670
Funder
Australian Research Council
Funding Amount
$370,410.00
Summary
Scalable Bayesian model selection for massive data sets. This project will develop highly innovative, efficient and ultimately effective methodology for Bayesian model selection for large-scale problems which commonly arise in biostatistics and bioinformatics. The resulting methodology will dramatically reduce the duration of analyses in these areas from days or weeks to minutes or hours.
Semiparametric Regression for Streaming Data. Semiparametric regression converts large and complex data-sets into interpretable summaries from which sound decisions can be made. This project tackles semiparametric regression analysis of streaming data - where the data are so voluminous that they may not be storable in standard computer memory and therefore need to be processed rapidly on arrival and then discarded. Effective solutions necessitate a rethinking of semi-parametric regression and ne ....Semiparametric Regression for Streaming Data. Semiparametric regression converts large and complex data-sets into interpretable summaries from which sound decisions can be made. This project tackles semiparametric regression analysis of streaming data - where the data are so voluminous that they may not be storable in standard computer memory and therefore need to be processed rapidly on arrival and then discarded. Effective solutions necessitate a rethinking of semi-parametric regression and new approaches will be developed. The project will also develop novel theory and methodology for robotics applications. It will allow analysis of streaming and massive data sets that would not be possible using currently available methods, opening up new applications.Read moreRead less
Fast approximate inference methods for flexible regression. This project will gear up statisticians to meet challenges brought about by rapid technological change in the 2010s and beyond. It will produce new methodology and theory for fast and flexible analysis of large and complex data sets.
Fast approximate inference methods: new algorithms, applications and theory. This project aims to develop new algorithms and theory for fast approximate inference and lay down infrastructure to aid future extensions. Fast approximate inference methods are a principled and extensible means of fitting large and complex statistical models to big data sets. They come into their own in applications where speed is paramount and traditional approaches are not feasible. The project aims to lead to prac ....Fast approximate inference methods: new algorithms, applications and theory. This project aims to develop new algorithms and theory for fast approximate inference and lay down infrastructure to aid future extensions. Fast approximate inference methods are a principled and extensible means of fitting large and complex statistical models to big data sets. They come into their own in applications where speed is paramount and traditional approaches are not feasible. The project aims to lead to practical outcomes from better business decision-making for insurance data warehouses, to improved medical imaging technology.Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE130100291
Funder
Australian Research Council
Funding Amount
$374,595.00
Summary
Adaptive control of stochastic queueing networks. Queues of items competing for service appear on the road, in health-care, in manufacturing and in communication systems. This project will set up methodology for adaptive control and resource allocation for stochastic queueing network models applicable to a variety of scenarios accounting for parameter uncertainty.
New statistical tools for mineral exploration targeting and validation. Exploration for new mineral resources depends on information gleaned from geological survey data. This project confronts important, unsolved statistical problems in the analysis of geological survey data which have direct impact on exploration targeting.