Classification of Microarray Gene-Expression Data. The broad aim is to provide statistical methodology for the classification of microarray gene-expression data. Microarrays are part of a new biotechnology that allows the monitoring of expression levels for thousands of genes simultaneously. The explosion in microarrays has produced massive quantities of data that require new statistical techniques for analysis in order to exploit their enormous scientific potential. One of the main uses of ....Classification of Microarray Gene-Expression Data. The broad aim is to provide statistical methodology for the classification of microarray gene-expression data. Microarrays are part of a new biotechnology that allows the monitoring of expression levels for thousands of genes simultaneously. The explosion in microarrays has produced massive quantities of data that require new statistical techniques for analysis in order to exploit their enormous scientific potential. One of the main uses of the methodology to be developed is to expedite the discovery of new subclasses of diseases. Another is to provide prediction rules for the diagnosis and treatment of diseases.Read moreRead less
Motor Unit Numbers Estimation (MUNE) using Bayesian statistical methodology for monitoring of progression of neuromuscular diseases. A means of objectively measuring the pathology of a neuromuscular disease involving motor unit loss, such as motor neuron disease, is much needed. This will be achieved by using newly developed electrophysiological techniques and developing new Bayesian statistical methodology to determine the number of motor units that supply a muscle. Our innovations will reliabl ....Motor Unit Numbers Estimation (MUNE) using Bayesian statistical methodology for monitoring of progression of neuromuscular diseases. A means of objectively measuring the pathology of a neuromuscular disease involving motor unit loss, such as motor neuron disease, is much needed. This will be achieved by using newly developed electrophysiological techniques and developing new Bayesian statistical methodology to determine the number of motor units that supply a muscle. Our innovations will reliably determine the number of motor units that supply a muscle in both normal subjects and in diseased patients with loss of motor nerves. This will enable the monitoring of disease progression. An outcome will be a software package that can be used with standard electrophysiology machines.Read moreRead less
Applications of Bayesian methods in Genomics and Comparative Genomics. Bayesian statistics provides a unified and versatile approach to problems of data analysis, inference and hypothesis testing. This project will involve the application of Bayesian methods to four topics of commercial and scientific importance in the fields of Genomics and Comparative Genomics. The four topics are: data analysis for a novel DNA sequencing technology, investigating genomic structure using multiple change-point ....Applications of Bayesian methods in Genomics and Comparative Genomics. Bayesian statistics provides a unified and versatile approach to problems of data analysis, inference and hypothesis testing. This project will involve the application of Bayesian methods to four topics of commercial and scientific importance in the fields of Genomics and Comparative Genomics. The four topics are: data analysis for a novel DNA sequencing technology, investigating genomic structure using multiple change-point analysis, phlogenetic inference with multiple genes and detection of incongruent phylogenies. The overall goal of the project is to advance understanding of the structure, function and evolution of genomes.Read moreRead less
Making the most of remotely sensed data: Bayesian spatio-temporal models for enhanced natural resource management and design. This research will provide methods for cost-efficient spatio-temporal data collection and analysis, with increased capacity for better decision-making about managing Australia’s natural resources. Through the Linkage partners’ networks, these benefits will be disseminated throughout regional bodies, government agencies and research groups across and outside Australia. The ....Making the most of remotely sensed data: Bayesian spatio-temporal models for enhanced natural resource management and design. This research will provide methods for cost-efficient spatio-temporal data collection and analysis, with increased capacity for better decision-making about managing Australia’s natural resources. Through the Linkage partners’ networks, these benefits will be disseminated throughout regional bodies, government agencies and research groups across and outside Australia. The statistical techniques derived in this project will enhance Australia’s capability in the national priority area of mathematical sciences, and will contribute to knowledge in a wide range of disciplines including natural resources, medicine and genetics. Read moreRead less
New Methods in Theory and Cosmic Applications of Spherical Random Fields. This project aims to investigate and model spherical random fields which are described as solutions of stochastic differential equations on a sphere or a ball. The project plans to study properties and develop spectral analysis of these solutions. It then plans to use the obtained theoretical results to construct new methods for numerical approximation and statistical estimation of these random fields. In particular, it pl ....New Methods in Theory and Cosmic Applications of Spherical Random Fields. This project aims to investigate and model spherical random fields which are described as solutions of stochastic differential equations on a sphere or a ball. The project plans to study properties and develop spectral analysis of these solutions. It then plans to use the obtained theoretical results to construct new methods for numerical approximation and statistical estimation of these random fields. In particular, it plans to develop novel asymptotic and statistical methodology for tensor random fields. The project will apply the results to model and analyse cosmic microwave background data. Expected outcomes will improve the accuracy in determining cosmological parameters and provide novel tools for better understanding of the universe during its early stages.Read moreRead less
New Developments for Bayesian statistical models and computational methods. Bayesian methods of statistical analysis provide a flexible theory for addressing inference in the presence of uncertainty. Consequently Bayesian methods have enabled scientific discovery in areas characterised as complex systems where new developments in modelling and computational methods have been crucial. Significant barriers to further success involve challenges in formulating and validating models, dealing with l ....New Developments for Bayesian statistical models and computational methods. Bayesian methods of statistical analysis provide a flexible theory for addressing inference in the presence of uncertainty. Consequently Bayesian methods have enabled scientific discovery in areas characterised as complex systems where new developments in modelling and computational methods have been crucial. Significant barriers to further success involve challenges in formulating and validating models, dealing with large data sets, and developing efficient computational methods. The principal aim of this project is to develop new Bayesian modelling and computational methodology which address these challenges with broad application.Read moreRead less
International Networks in Applied Bayesian Statistics: improving Australia''s knowledge through intelligent data analysis and modelling. National benefits of this project are fourfold: (i) new international networks between Australia, Southern Africa, France and USA in the priority area of mathematical sciences; (ii) state-of-the-art Bayesian statistical methods for integrating and analyzing non-standard data and diverse information sources, including expert opinion, in order to solve complex pr ....International Networks in Applied Bayesian Statistics: improving Australia''s knowledge through intelligent data analysis and modelling. National benefits of this project are fourfold: (i) new international networks between Australia, Southern Africa, France and USA in the priority area of mathematical sciences; (ii) state-of-the-art Bayesian statistical methods for integrating and analyzing non-standard data and diverse information sources, including expert opinion, in order to solve complex problems in environment, industry, health, defence; (iii) direct contribution to solution of global environmental problems, specifically water quality, threatened species and environmental risk; (iv) superior training of the next generation of the global community of researchers in applied statistics.Read moreRead less
Linkage Infrastructure, Equipment And Facilities - Grant ID: LE0453870
Funder
Australian Research Council
Funding Amount
$102,900.00
Summary
Social science advanced data modelling, analysis and visualisation facility. This is an integrated facility for advanced social science data analysis, modelling and visualisation located at The University of Queensland. The collaborating institutions are UQ, ANU and Griffith University. The facility promotes deep collaborations between social scientists and quantitative methodologists (statisticians, biostatisticians, econometricians) to enable leading edge quantitative analyses of survey, spati ....Social science advanced data modelling, analysis and visualisation facility. This is an integrated facility for advanced social science data analysis, modelling and visualisation located at The University of Queensland. The collaborating institutions are UQ, ANU and Griffith University. The facility promotes deep collaborations between social scientists and quantitative methodologists (statisticians, biostatisticians, econometricians) to enable leading edge quantitative analyses of survey, spatial and population data, new developments in quantitative methodology for statistical modelling and data visualisation, enhanced international research linkages and advanced postgraduate research training.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
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