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
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
Threshold Decisions in Determining Whether to Prosecute Child Sexual Abuse. The objective of this project is new knowledge about the way police and prosecutors make decisions about the prosecution of child sexual assault that could be used to influence policy and practice. Few cases of child sexual abuse reported to the police ever go to court but recent research in New South Wales for the Royal Commission indicates that the proportion has declined sharply over the last decade or so. This projec ....Threshold Decisions in Determining Whether to Prosecute Child Sexual Abuse. The objective of this project is new knowledge about the way police and prosecutors make decisions about the prosecution of child sexual assault that could be used to influence policy and practice. Few cases of child sexual abuse reported to the police ever go to court but recent research in New South Wales for the Royal Commission indicates that the proportion has declined sharply over the last decade or so. This project aims to examine how police and prosecutors decide which cases proceed and why, and how they confer with each other as well as when and how they consult with complainants and their families. This project plans to also develop and test practice tools and principles for police and prosecutors with expected benefits for both them and the families involved.Read moreRead less
Statistical methods for quantifying variation in spatiotemporal areal data. This project aims to develop new statistical methods for extracting insights into spatial and temporal variation in areal data. These tools will extend the Australian Cancer Atlas which provides small area estimates for 20 cancers across Australia. The project is significant because it will allow government and other organisations to reap dividends from investment in collecting spatial information and it will enable mode ....Statistical methods for quantifying variation in spatiotemporal areal data. This project aims to develop new statistical methods for extracting insights into spatial and temporal variation in areal data. These tools will extend the Australian Cancer Atlas which provides small area estimates for 20 cancers across Australia. The project is significant because it will allow government and other organisations to reap dividends from investment in collecting spatial information and it will enable modelled small-area estimates to be released without compromising confidentiality. The expected outcomes include new statistical knowledge and new insights into cancer. The results will benefit the many disciplines, managers and policy makers that make decisions based on geographic data mapped over space and time. Read moreRead less
Discovery Early Career Researcher Award - Grant ID: DE240101190
Funder
Australian Research Council
Funding Amount
$451,000.00
Summary
Innovating and Validating Scalable Monte Carlo Methods. This project aims to develop innovative scalable Monte Carlo methods for statistical analysis in the presence of big data or complex mathematical models. Existing approaches to scalable Monte Carlo are only approximate, and their inaccuracies are difficult to quantify. This can have a detrimental impact on data-based decision making. The expected outcomes of this project are scalable Monte Carlo methods that are more accurate, fast and capa ....Innovating and Validating Scalable Monte Carlo Methods. This project aims to develop innovative scalable Monte Carlo methods for statistical analysis in the presence of big data or complex mathematical models. Existing approaches to scalable Monte Carlo are only approximate, and their inaccuracies are difficult to quantify. This can have a detrimental impact on data-based decision making. The expected outcomes of this project are scalable Monte Carlo methods that are more accurate, fast and capable of quantifying inaccuracies. Scientists and decision-makers will benefit from the ability to obtain timely, reliable insights for challenging applications.Read moreRead less
Precision ecology: the modern era of designed experiments in plant ecology. This project aims to develop the field of precision ecology, forging a new era of designed experiments where sampling is informed by research questions and what is known about the ecological process being studied. Through the development of novel statistical methods, new experiments globally will be designed to answer important ecological questions including what influence abiotic and biotic factors have on plant commun ....Precision ecology: the modern era of designed experiments in plant ecology. This project aims to develop the field of precision ecology, forging a new era of designed experiments where sampling is informed by research questions and what is known about the ecological process being studied. Through the development of novel statistical methods, new experiments globally will be designed to answer important ecological questions including what influence abiotic and biotic factors have on plant communities over time and different spatial scales. Expected outcomes include new methods and tools that will modernise how future experiments will be conducted in plant ecology. This will provide significant transdisciplinary benefits including new statistical methods that target scientific discovery in ecological studies.Read moreRead less
Judges' work, place and psychological health - a national view. This project aims to address the human, juridical and financial costs of judicial officers’ work-related psychological harm. This harm is implicated in early retirement, sick leave and suicide. It threatens appropriate courtroom conduct, procedural fairness and impartial adjudication. The project seeks to generate new knowledge of the stress judicial officers experience and the individual and institutional mechanisms for managing st ....Judges' work, place and psychological health - a national view. This project aims to address the human, juridical and financial costs of judicial officers’ work-related psychological harm. This harm is implicated in early retirement, sick leave and suicide. It threatens appropriate courtroom conduct, procedural fairness and impartial adjudication. The project seeks to generate new knowledge of the stress judicial officers experience and the individual and institutional mechanisms for managing stressors, combining socio-legal and psychological approaches. Expected outcomes include evidence-based understandings to inform recruitment and retention strategies specific to this highly specialized workforce. This should provide significant benefits for judges’ work capacities and courts' delivery of justice.Read moreRead less
Creating perceptual experts in Australia's policing and security agencies. This project aims to create the next generation of experts in Australia’s policing and national security agencies, by improving crime scene evidence interpretation. Agencies are under pressure to develop more rigorous training practices that go beyond mere intuition and tradition. This project will use a novel approach that directs learning toward the most diagnostic perceptual cues. Expected outcomes include a solid empi ....Creating perceptual experts in Australia's policing and security agencies. This project aims to create the next generation of experts in Australia’s policing and national security agencies, by improving crime scene evidence interpretation. Agencies are under pressure to develop more rigorous training practices that go beyond mere intuition and tradition. This project will use a novel approach that directs learning toward the most diagnostic perceptual cues. Expected outcomes include a solid empirical basis for national training programs designed to create experts that are accurate, reliable, and continuously improving. Improving the training of experts will ensure the integrity of forensics as evidentiary tools available to police, lead to more reliable courtroom convictions and help safeguard Australia from terrorism and crime.Read moreRead less
A Novel Approach to Semi-Supervised Statistical Machine Learning. Recent successes in the construction of classifiers for making diagnoses and predictions are due in part to their using much data labelled with respect to their class of origin. But typically there are little labelled data but plentiful unlabelled data. The goal of semi-supervised learning (SSL) is to leverage large amounts of unlabelled data to improve the performance using only small labelled datasets and so SSL is of paramount ....A Novel Approach to Semi-Supervised Statistical Machine Learning. Recent successes in the construction of classifiers for making diagnoses and predictions are due in part to their using much data labelled with respect to their class of origin. But typically there are little labelled data but plentiful unlabelled data. The goal of semi-supervised learning (SSL) is to leverage large amounts of unlabelled data to improve the performance using only small labelled datasets and so SSL is of paramount importance to applications where it is expensive or impractical to obtain much labelled data. The project is to develop a novel SSL approach that adopts a missingness mechanism for the missing labels to build a classifier that not only improves accuracy but it can be greater than if the missing labels were known.
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Discovery Early Career Researcher Award - Grant ID: DE210100357
Funder
Australian Research Council
Funding Amount
$427,320.00
Summary
What determines your face identification accuracy? Accurate face identification underpins normal social functioning and important identity verification procedures in society, government and the justice system. However, there is little understanding of the cognitive processes that give rise to individual differences in face identification. This project aims to develop a new cognitive model that characterises how holistic and part-based processing combine to determine individual differences in fac ....What determines your face identification accuracy? Accurate face identification underpins normal social functioning and important identity verification procedures in society, government and the justice system. However, there is little understanding of the cognitive processes that give rise to individual differences in face identification. This project aims to develop a new cognitive model that characterises how holistic and part-based processing combine to determine individual differences in face identification. Expected benefits include advancing knowledge of human face perception, and evidence-based training and personnel selection tools to improve decision accuracy, help police prevent crime and terrorism, and avoid wrongful conviction of innocent suspects.Read moreRead less