55 citations · 121 across the 24 of their papers we have counts for
25 papers
Unsupervised anomaly detection in spatio-temporal stream network sensor data
Edgar Santos-Fernandez, Jay M. Ver Hoef, Erin E. Peterson +6
The use of in-situ digital sensors for water quality monitoring is becoming increasingly common worldwide. While these sensors provide near real-time data for science, the data are…
Bayesian Design for Sampling Anomalous Spatio-Temporal Data
Katie Buchhorn, Kerrie Mengersen, Edgar Santos-Fernandez +1
Data collected from arrays of sensors are essential for informed decision-making in various systems. However, the presence of anomalies can compromise the accuracy and reliability…
Communicating uncertainty in Indigenous sea Country monitoring with Bayesian statistics: towards more informed decision-making
Katherine Cure, Diego R Barneche, Martial Depczynski +14
First Nations Australians have a cultural obligation to look after land and sea Country, and Indigenous-partnered science is beginning to drive socially inclusive initiatives in co…
A general Bayesian approach to design adaptive clinical trials with time-to-event outcomes
James M. McGree, Antony M. Overstall, Mark Jones +1
Clinical trials are an integral component of medical research. Trials require careful design to, for example, maintain the safety of participants, use resources efficiently and all…
Robust simulation design for generalized linear models in conditions of heteroscedasticity or correlation
Andrew Gill, David J. Warne, Antony M. Overstall +2
A meta-model of the input-output data of a computationally expensive simulation is often employed for prediction, optimization, or sensitivity analysis purposes. Fitting is enabled…
Being Bayesian in the 2020s: opportunities and challenges in the practice of modern applied Bayesian statistics
Joshua J. Bon, Adam Bretherton, Katie Buchhorn +12
Building on a strong foundation of philosophy, theory, methods and computation over the past three decades, Bayesian approaches are now an integral part of the toolkit for most sta…
Model-robust Bayesian design through Generalised Additive Models for monitoring submerged shoals
Dilishiya De Silva, Rebecca Fisher, Ben Radford +2
Optimal sampling strategies are critical for surveys of deeper coral reef and shoal systems, due to the significant cost of accessing and field sampling these remote and poorly und…
A model robust sub-sampling approach for Generalised Linear Models in Big data settings
Amalan Mahendran, Helen Thompson, James M. McGree
In today's modern era of Big data, computationally efficient and scalable methods are needed to support timely insights and informed decision making. One such method is sub-samplin…
Bayesian Design with Sampling Windows for Complex Spatial Processes
Katie Buchhorn, Kerrie Mengersen, Edgar Santos-Fernandez +2
Optimal design facilitates intelligent data collection. In this paper, we introduce a fully Bayesian design approach for spatial processes with complex covariance structures, like…
SSNbayes: An R package for Bayesian spatio-temporal modelling on stream networks
Edgar Santos-Fernandez, Jay M. Ver Hoef, James M. McGree +3
Spatio-temporal models are widely used in many research areas from ecology to epidemiology. However, most covariance functions describe spatial relationships based on Euclidean dis…
Detection of cybersecurity attacks through analysis of web browsing activities using principal component analysis
Insha Ullah, Kerrie Mengersen, Rob J Hyndman +1
Organizations such as government departments and financial institutions provide online service facilities accessible via an increasing number of internet connected devices which ma…
Bayesian spatio-temporal models for stream networks
Edgar Santos-Fernandez, Jay M. Ver Hoef, Erin E. Peterson +3
Spatio-temporal models are widely used in many research areas including ecology. The recent proliferation of the use of in-situ sensors in streams and rivers supports space-time wa…
Bayesian design for minimising prediction uncertainty in bivariate spatial responses with applications to air quality monitoring
S. G. Jagath Senarathne, Werner G. Müller, James M. McGree
Model-based geostatistical design involves the selection of locations to collect data to minimise an expected loss function over a set of all possible locations. The loss function…
SSNdesign -- an R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks
Alan R. Pearse, James M. McGree, Nicholas A. Som +4
Streams and rivers are biodiverse and provide valuable ecosystem services. Maintaining these ecosystems is an important task, so organisations often monitor the status and trends i…
Bayesian adaptive N-of-1 trials for estimating population and individual treatment effects
S. G. Jagath Senarathne, Antony M. Overstall, James M. McGree
This article proposes a novel adaptive design algorithm that can be used to find optimal treatment allocations in N-of-1 clinical trials. This new methodology uses two Laplace appr…
Factors associated with injurious from falls in people with early stage Parkinson's disease
Sarini Abdullah, James McGree, Nicole White +2
Falls are common in people with Parkinson's disease (PD) and have detrimental effects which can lower the quality of life. While studies have been conducted to learn about falling…
Profile regression for subgrouping patients with early stage Parkinson's disease
Sarini Abdullah, James McGree, Nicole White +2
Falls are detrimental to people with Parkinson's Disease (PD) because of the potentially severe consequences to the patients' quality of life. While many studies have attempted to…
Assessing the predictive ability of the UPDRS for falls classification in early stage Parkinson's disease
Sarini Abdullah, Nicole White, James McGree +2
Identification of risk factors associated with falls in people with Parkinson's Disease (PD) is important due to their high risk of falling. In this study, various ways of utilizin…
Bayesian decision-theoretic design of experiments under an alternative model
Antony M. Overstall, James M. McGree
Traditionally Bayesian decision-theoretic design of experiments proceeds by choosing a design to minimise expectation of a given loss function over the space of all designs. The lo…
A synthetic likelihood-based Laplace approximation for efficient design of biological processes
Mahasen Dehideniya, Antony M. Overstall, Chris C. Drovandi +1
Complex models used to describe biological processes in epidemiology and ecology often have computationally intractable or expensive likelihoods. This poses significant challenges…
Predicting Sediment and Nutrient Concentrations in Rivers Using High Frequency Water Quality Surrogates
Catherine Leigh, Sevvandi Kandanaarachchi, James M. McGree +4
A particular focus of water-quality monitoring is the concentrations of sediments and nutrients in rivers, constituents that can smother biota and cause eutrophication. However, th…
A framework for automated anomaly detection in high frequency water-quality data from in situ sensors
Catherine Leigh, Omar Alsibai, Rob J. Hyndman +9
River water-quality monitoring is increasingly conducted using automated in situ sensors, enabling timelier identification of unexpected values. However, anomalies caused by techni…
Bayesian design of experiments for intractable likelihood models using coupled auxiliary models and multivariate emulation
Antony M. Overstall, James M. McGree
A Bayesian design is given by maximising an expected utility over a design space. The utility is chosen to represent the aim of the experiment and its expectation is taken with res…
An approach for finding fully Bayesian optimal designs using normal-based approximations to loss functions
Antony M. Overstall, James M. McGree, Christopher C. Drovandi
The generation of decision-theoretic Bayesian optimal designs is complicated by the significant computational challenge of minimising an analytically intractable expected loss func…