Kerrie Mengersen | ARC Centre of Excellence for Mathematical and Statistical Frontiers

ACEMS Deputy Director, Chief Investigator

Queensland University of Technology

My focus is on using and developing new statistical and computational methods that can help to solve complex problems in the real world. These problems are in the fields of environment, genetics, health and medicine, and industry. My research areas include Bayesian statistics, hierarchical modelling and complex systems. What’s best about my job? That I can work with a diverse range of people doing outstanding things in many different areas, and contribute expertise in an important component of their work: making the best use of their data to help them make better decisions.

Research Interests:

Applied statistics

Bayesian networks

Bayesian statistical modelling

Complex systems modelling

computational methods and applications

Digging postholes

Qualifications:

PhD (University of New England)

BA(Hons) (University of New England)

Projects

Helping Save Jaguars with a Virtual Peruvian Jungle

ACEMS Deputy Director Kerrie Mengersen is leading a team of researchers to try to save the jaguar population in Peru. Her project combines the use of statistics, mathematics, and virtual reality technology to help leaders make informed decisions about the threatened species, and the land it depends on.

Decision tree methods for filling missing data in satellite images and identifying woodland

ACEMS researchers have produced a fast and highly accurate approach for filling data gaps in free satellite images due to clouds and classifying land cover.

A Feature‐Based Procedure for Detecting Technical Outliers in Water‐Quality Data From In Situ Sensors

Water-quality sensors are exposed to changing environments and extreme weather conditions and thus are prone to errors, including failure. These technical errors make data unreliable and untrustworthy and affect performance of any subsequent data analysis. ACEMS researchers, led by Priyanga Dilini Talagala, have proposed a feature based procedure, named oddwater, for detecting technical outliers in water-quality data derived from in situ sensors.

Bayesian Approaches to Modelling and Analysis of Big Data

Bayesian methods for modelling and analysis of data are now well established. However, as with many statistical methods, their applicability in the context ‘big data’ is still being explored. This project will consider four questions that relate to ‘scalable Bayes’, that is, Bayesian models and computational methods that scale up to big data problems.

This project links to a number of other projects across the Centre.

Development of the Australian Cancer Atlas: spatial modelling, visualisation, and reporting of estimates

This project explains the rationale behind a lot of the decisions involved in creating the Australian Atlas of Cancer.

Forecasting intensifying disturbance effects on coral reefs

ACEMS research combines state-of-art methodologies for coral reef data collection, remote sensing and statistical modelling to predict the future ecological status of the reefs within the Great Barrier Reef as the incidence of multiple disturbances continues to increase.

Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic

Using Bayesian statistics and mathematical models to assess the global response to COVID-19. This analysis uses data from 158-countries.

How are people modelling dengue fever over small-areas and time?

Dengue fever is one of the most serious health issues in the Asia-Pacific region, affecting more than 100-million people every year. ACEMS Researchers carried out a systematic review of the different spatial and spatio-temporal models that are looked into the disease.

Jump test tool to predict athletic performance

ACEMS researchers develop statistical method to predict fatigue in athletes to help prevent over-training, or worse, injury.

Monitoring through many eyes: Integrating datasets to improve monitoring of the Great Barrier Reef

Enormous effort is invested in monitoring the Great Barrier Reef (GBR), but data collection is currently fragmented over dozens of publicly and privately funded organisations, with data collected using different methods and for different purposes. As a result, the data are rarely analysed together.

Multivariate Bayesian meta‑analysis: joint modelling of multiple cancer types using summary statistics

This research provides a tool so that people can use readily available data from online cancer atlases, and understand patterns for multiple cancer types together.

Stochastic spatial random forest (SS-RF) for interpolating probabilities of missing land cover data

How do you measure something you can’t see?

That’s the aim of new research just published by ACEMS’ researchers in the Journal of Big Data. Led by Jacinta Holloway Brown from ACEMS at QUT, the researchers developed a new statistical method to predict forest cover in satellite images where portions of the image are blocked by cloud cover. Not only that, the new method also calculates a probability to show how confident the prediction is.

Publications

Books

2015

[Anonymous] (2015). Biosecurity Surveillance: Quantitative Approaches. (Jarrad, F., Low-Choy S., & Mengersen KL., Ed.). doi: 10.1079/9781780643595.0000

Book Chapters

2020

Santos-Fernández, E., Mengersen KL., & Wu P. (2020). Bayesian Methods in Sport Statistics. (Balakrishnan, N.., Colton T., Everitt B., Piegorsch W., Ruggeri F., & Teugels J. L., Ed.).Wiley StatsRef: Statistics Reference Online. 1 - 8. doi: 10.1002/9781118445112.stat08179

Xie, H-B., Li C., Da Xu R. Yi, & Mengersen KL. (2020). Robust Kernelized Bayesian Matrix Factorization for Video Background/Foreground Separation. (Nicosia, G., Pardalos P., Umeton R., Giuffrida G., & Sciacca V., Ed.).Lecture Notes in Computer Science: Machine Learning, Optimization, and Data Science. 1, 484 - 495. doi: 10.1007/978-3-030-37599-7_40

Vercelloni, J., M. Caley J., & Mengersen KL. (2020). Thresholds of Coral Cover That Support Coral Reef Biodiversity. (Mengersen, KL., Pudlo P., & Robert C. P., Ed.).Lecture Notes in Mathematics: Case Studies in Applied Bayesian Data Science. 2259, 385 - 398. doi: 10.1007/978-3-030-42553-1_16

2019

Mengersen, KL., Duncan E., Arbel J., Alston-Knox C., & White N. (2019). Applications in Industry. (Fruhwirth-Schnatter, S., Celeux G., & Robert C. P., Ed.).Handbook of Mixture Analysis.

2018

Drovandi, C. C., Grazian C., Mengersen KL., & Robert C. (2018). Approximating the Likelihood in ABC. Handbook of Approximate Bayesian Computation. 321-368.

2015

Murray, J., Whittle P., Jarrad F., Barrett S., Stoklosa R., & Mengersen KL. (2015). Design of a surveillance system for non-indigenous species on Barrow Island: plants case study. (Nikles, J., & Mitchell G., Ed.).Biosecurity surveillance: quantitative approaches. 203-216. doi: 10.1079/9781780643595.0203

van Havre, Z., & Whittle P. (2015). Designing surveillance for emergency response. (Jarrad, F., Low-Choy S., & Mengersen KL., Ed.).Biosecurity surveillance: quantitative approaches. 123-133. doi: 10.1079/9781780643595.0123

Mittnty, M., Whittle P., Burgman P., & Mengersen KL. (2015). The role of surveillance in evaluating and comparing international quarantine systems. (Jarrad, F., Low-Choy S., & Mengersen KL., Ed.).Biosecurity Surveillance: Quantitative Approaches. 137-150. doi: 10.1079/9781780643595.0137

Mengersen, KL., McGree J., & Schmid C. H. (2015). Systematic Review and Meta-analysis Using N-of-1 Trials. (Nikles, J., & Mitchell G., Ed.).The Essential Guide to N-of-1 Trials in Health. 211-231. doi: 10.1007/978-94-017-7200-6_16

Mengersen, KL., McGree J., & Schmid C. H. (2015). Statistical Analysis of N-of-1 Trials. (Nikles, J., & Mitchell G., Ed.).The Essential Guide to N-of-1 Trials in Health. 135-153. doi: 10.1007/978-94-017-7200-6_12

Johnson, S., Mengersen KL., Ormsby M., & Whittle P. (2015). Using Bayesian networks to model surveillance in complex plant and animal health systems. (Jarrad, F., Low-Choy S., & Mengersen KL., Ed.).Biosecurity Surveillance: Quantitative Approaches. 278-295. doi: 10.1079/9781780643595.0278

Quinlan, M., Stanaway M., & Mengersen KL. (2015). Biosecurity surveillance in agriculture and environment: a review. (Jarrad, F.., Low-Choy S.., & Mengersen K.., Ed.).Biosecurity surveillance: quantitative approaches. 9-42. doi: 10.1079/9781780643595.0009

Invited talks, refereed proceedings and other conference outputs

2020

Adams, M. P., Sisson S., O'Brien K. R., Helmstedt K. J., Baker C. M., Koh E. J. Y., et al. (2020). Propagating uncertainty through to model forecasts: deterministic Lotka-Volterra systems as a case study. Quantitative Ecology Virtual Meeting 2020.

Warne, D. J., Crossman K. A., Jin W., Mengersen KL., Osborne K., Simpson M. J., et al. (2020). Identification of two-phase recovery in hard corals across the Great Barrier Reef. ACEMS 2020 Virtual Retreat.

Warne, D., Ebert A., Drovandi C., Mira A., & Mengersen KL. (2020). Characterisation of the global response to the COVID-19 pandemic. ACEMS COVID‐19 Research Workshop.

2019

Mengersen, KL. (2019). Virtual reality meets data science. OzViz 2019.

Jones, S., Hargrave C., Deegan T., Holt T., & Mengersen KL. (2019). Predicting the need for hydrogel spacers in prostate radiotherapy - a statistical modelling approach. Australian Society of Medical Imaging and Radiation Therapy National Conference. doi: 10.1002/jmrs.2019.66.issue-S1,10.1002/jmrs.324

Peterson, E., Mengersen KL., Vercelloni J., & Brown R. (2019). Virtual Reef Diver. International Workshop on Spatial Statistics.

Shausan, A.., Mengersen KL., Drovandi C.., & Aaskov J.. (2019). Minimising the severity of dengue Serotype 1 infection by transmissible interfering particles. ACEMS Node Planning Day.

Cramb, S., Duncan EW., Aitken JA., Mengersen KL., & Baade PD. (2019). Small-area melanoma incidence patterns across Australia: the thick and thin of it. Australasian Epidemiological Association Annual Scientific Meeting 2019.

Jahan, F., Duncan E., Cramb S., Mengersen KL., & Baade P. (2019). Augmeting Disease Maps: a Bayesian meta-analysis approach. Young Statisticians Conference 2019.

Mengersen, KL. (2019). Bayesian Statistical Analysis of Large Images. Data Science Down Under 2019.

Peterson, E., Mengersen KL., Vercelloni J., & Brown R. (2019). Combining citizen science and professional monitoring data to conserve and sustainably use marine resources. 62nd ISI World Statistics Congress.

Shausan, A.., Mengersen KL., Drovandi C.., & Aaskov J.. (2019). Minimising Dengue Spread by Transmissible Interfering Particles. Young Statistician Conference 2019.

Jahan, F., & Mengersen KL. (2019). Bayesian Empirical Likelihood Spatial Model applying Leroux Structure. Young Statisticians Conference 2019.

Aswi, A.., Cramb S., Hu W., White G., & Mengersen KL. (2019). Bayesian spatio-temporal conditional autoregressive localised model. ACEMS Retreat 2019.

Mengersen, KL. (2019). Which way should I cycle? A case study in Bayesian modelling for decision-making under uncertainty study. ICSMTR2019: 3rd International Conference on Statistics, Mathematics, Teaching, and Research.

Shausan, A.., Mengersen KL., Drovandi C.., & Aaskov J.. (2019). Minimising Dengue Spread Using TIP Therapy. Bayes on the Beach 2019.

Gilholm, P., Mengersen KL., & Thompson H. (2019). Identifying latent subgroups of children with developmental delay using Bayesian sequential updating and Dirichlet process mixture modelling - poster Bayes on the Beach. Bayes on the Beach 2019.

Aswi, A.., Cramb S., Hu W., White G., & Mengersen KL. (2019). Evaluating the interplay between clusters, climatic covariates and spatial priors in spatio-temporal modelling of dengue in Makassar, Indonesia. Bayes on the Beach 2019.

Mengersen, KL. (2019). Bayesian Learning for Decision Making in the Big Data Era. Bayes on the Beach 2019.

Moores, M. T., Nicholls G. K., Pettitt A. N., & Mengersen KL. (2019). Bayesian Indirect Likelihood for the Potts Model. The 12th International Conference on Monte Carlo Methods and Applications.

Aswi, A.., Cramb S., Hu W., White G., & Mengersen KL. (2019). Temporal Modeling of Dengue Fever: A Comprehensive Literature Review. 3rd International Conference on Mathematics, Sciences, Technology, Education and their application (ICSMTEA) in Conjunction with 1st International Symposium on Green Materials & Technology (ISGMT).. 967, 15-21. doi: 10.4028/www.scientific.net/MSF.967.15

Jahan, F., & Mengersen KL. (2019). Bayesian Empirical Likelihood Spatial Model applying Leroux Structure. Bayes on the Beach 2019.

Mengersen, KL. (2019). The Challenges, Discoveries and Examples of ABC. The 12th International Conference on Monte Carlo Methods and Applications.

Cramb, S., Duncan EW., Baade PD., & Mengersen KL. (2019). Computing the Australian Cancer Atlas: getting it ‘just right’. The 12th International Conference on Monte Carlo Methods and Applications.

Jahan, F., Duncan E., Cramb S., Baade P., & Mengersen KL. (2019). Multivariate Bayesian meta-analysis model to analyse estimated cancer incidence. ACEMS Students and ECR Retreat.

Wu, P. Pao- Yen, Ruggeri F., & Mengersen KL. (2019). Observational Uncertainty in Bayesian Networks and State Space Models. Bayes on the Beach 2019.

2018

Aswi, A.., Cramb S., Hu W., White G., & Mengersen KL. (2018). Bayesian spatio-temporal models using R-INLA. ACEMS Retreat 2018.

Liquet, B.., Mengersen KL., Pettitt A.N., & Sutton M. (2018). Bayesian Variable Selection Regression Of Multivariate Responses For Group Data. Bayesian Statistics in the Big Data Era.

Cramb, S., Duncan EW., Baade PD., & Mengersen KL. (2018). Bayesian disease mapping in R. UseR! 2018.

Aswi, A.., Cramb S., Hu W., White G., & Mengersen KL. (2018). Comparison of different Bayesian spatio-temporal models using R packages. UseR! .

Jahan, F., Duncan E., Cramb S., Baade P. D., & Mengersen KL. (2018). Making More of Spatial Maps: A Bayesian Meta-analysis approach. ACEMS Students Retreat.

Ebert, A., Wu P., Mengersen KL., & Ruggeri F. (2018). A Review of Distances on Functional Datasets for Likelihood-Free Inference. Joint International Society for Clinical Biostatistics and Australian Statistical Conference 2018.

Xie, H., Van Huffel S., & Mengersen KL. (2018). A hybrid Bayesian low rank approximation model for nonlocal image denoising. EURASIP Summer School on Tensor-Based Signal Processing.

Aswi, A.., Cramb S., Hu W., White G., & Mengersen KL. (2018). Temporal modelling of dengue fever: A comprehensive literature review. 3rd International Conference on Mathematics, Sciences, Technology, Education and their application (ICSMTEA) in Conjunction with 1st International Symposium on Green Materials & Technology (ISGMT).

Holloway, J., Mengersen KL., & Helmstedt K. J. (2018). Spatial and machine learning methods of satellite imagery analysis for Sustainable Development Goals. IAOS-OECD Better Statistics for Better Lives.

Aswi, A.., Mengersen KL., Cramb S., Hu W., & White G. (2018). Comparing Spatio-Temporal models using CARBayes: An application to dengue fever in Makassar, Indonesia. The joint international society for clinical Biostatistics and Australian Statistical Conference 2018.

Jahan, F., & Mengersen KL. (2018). Bayesian Empirical Likelihood Spatial Model applying Leroux structure. International Conference on Bayesian Statistics in the Big Data Era Bayesian statistics in the era of Big Data .

Aswi, A.., Cramb S., Hu W., White G., & Mengersen KL. (2018). The impact of covariates on the grouping structure of a Bayesian spatio-temporal localised model. Australasian Applied Statistics Conference 2018.

Jahan, F., Duncan EW., Cramb S., Baade P. D., & Mengersen KL. (2018). Making More of Spatial Maps: A Bayesian meta-analysis approach. Workshop on Young Bayesians and Big Data for Social Good.

Wu, P. Pao- Yen, Garufi L., D'Auria S., & Mengersen KL. (2018). Understanding Changes in Physiological State During Triathlon Cycling Using Hidden Markov Models. ISBA World Meeting 2018.

Cramb, S., Duncan EW., Baade PD., & Mengersen KL. (2018). The Australian Cancer Atlas: Models and Messages. Joint International Society for Clinical Biostatistics and Australian Statistical Conference 2018.

2017

Mengersen, KL. (2017). Bayesian Maths and Stats meets new technology. WIMSIG women-in-mathematical-sciences conference.

Ebert, A., Wu P., Mengersen KL., & Ruggeri F. (2017). Efficient simulation of complex queueing systems with the R package queuecomputer. French R meeting.

Cespedes, M. I., McGree J., Drovandi C. C., Mengersen KL., Doecke J., & Fripp J. (2017). Spatio-temporal brain cortical patterns of Alzheimer's disease. Bayes on the Beach.

Aswi, A.., Cramb S., Duncan E., & Mengersen KL. (2017). Bayesian spatial estimation when areas are few. The international Biometric Society Australasian Region conference.

Mengersen, KL. (2017). Probabilistic Modelling in the real world. International Conference for Machine Learning.

Aswi, A.., Cramb S., Duncan E., & Mengersen KL. (2017). Evaluating the impact of small number of areas on spatial estimation. The Young Statistician Conference (YSC).

Mengersen, KL. (2017). Probabilistic Modelling in the real world. International Conference for Machine Learning.

White, N., Mengersen KL., & Lea R. (2017). Probabilistic deconvolution of microarray data using a hierarchical Bayesian model. Royal Statistical Society Conference 2017.

Mengersen, KL. (2017). Bayesian Maths and Stats meets new technology. ISI2017- 61st World Statistics Congress.

Mengersen, KL. (2017). Using Earth Observation Data for Offcial Statistics. 4th UN Conference on Big Data.

Reddan, T., Corness J., Powell J., Mengersen KL., & Harden F. (2017). Stumped? It Could be Stump Appendicitis (ePoster). The Society for Pediatric Radiology 60th Annual Meeting and Categorical Course. s228–s228. doi: 10.1007/s00247-017-3809-x

Mengersen, KL. (2017). Looking forward, back and sideways: the many directions of Bayesian forecasting. International Symposium on Forecasting, Cairns, June 25-28, 2017.

Ebert, A., Wu P., Dutta R., Mengersen KL., Ruggeri F., Mira A., et al. (2017). Approximate Bayesian Computation for Dynamic Queueing Networks. 5th Symposium on Games and Decisions in Reliability and Risk.

Mengersen, KL. (2017). Future Think. Young Statisticians Conference 2017.

Cramb, S., Baade P. D., & Mengersen KL. (2017). A new approach to small area cancer survival estimation. Geomed 2017.

Mengersen, KL. (2017). Looking forward, back and sideways: the many directions of Bayesian forecasting. International Symposium on Forecasting, Cairns, June 25-28, 2017.

Cespedes, M. I., McGree J., Drovandi C. C., Mengersen KL., Doecke J., & Fripp J. (2017). Spatio-temporal cortical brain patterns of Alzheimer's disease. The International Biometric Society Australasian Region Conference.

2016

Reddan, T., Corness J., Mengersen KL., & Harden F. (2016). The grumpy stump - ultrasound and stump appendicitis. 23rd Annual International Conference of the Australasian Sonographers Association. 52–53. doi: 10.1002/sono.12059

Mengersen, KL. (2016). How to be Bayesian in the Big Data era. Accelerated Data and Computing Workshop .

Mengersen, KL. (2016). A principled experimental design approach to modelling and of big data analysis. Spring Statistics Seminar Series.

Bednarz, T., Kim J., Brown R., James A., Burrage K., Clifford S., et al. (2016). Virtual reality for conservation. Web3D 2016: 21st International Conference on Web3D Technology. 177–178. doi: 10.1145/2945292.2945319

Arbel, J., Mengersen KL., Rousseau J., & Mengersen KL. (2016). Bayesian nonparametric dependent model for partially replicated data: The influence of fuel spills on species diversity. Conference in Honour of Professor Peter Hall.

Mengersen, KL. (2016). Priors and problems; using one to inform the other . The International Environmetrics Society Conference.

Mengersen, KL. (2016). Employing priors to learn about latent variable models. International Society for Bayesian Analysis 2016.

Mengersen, KL., Clifford S., Drovandi C. C., Harden F., Harden M., & Tierney N J. (2016). Bayesian Approaches in Occupational Health Surveillance. International Society for Bayesian Analysis.

Mengersen, KL. (2016). The Three Views of “I See”. Big Data, Visualisation, Art and Science Festival 2016.

Mengersen, KL. (2016). A principled experimental design approach to big data analysis. International Conference for Mathematics and Statistics.

Mengersen, KL., & Peterson E. (2016). Elicitation of expert information for conservation. The International Environmetrics Society.

Psaltis, S. T. P., Vercelloni J., Kim J., Brown R., James A., Burrage K., et al. (2016). Virtual reality for conservation. International Conference on Web3D Technology. 177–178. doi: 10.1145/2945292.2945319

Mengersen, KL. (2016). Combining new technology, big data, and maths and stats to fast track conservation. Big Data and Visual Analytics.

Mengersen, KL., Pettitt A.N., Sutton M., & Liquet B.. (2016). Bayesian Variable Selection Regression Of Multivariate Responses For Group Data. Australian Statistical Conference.

Reddan, T., Corness J., Mengersen KL., & Harden F. (2016). Appendiceal sonography in children: A retrospective analysis as a platform for potential implementation of diagnostic categories. 23rd Annual International Conference of the Australasian Sonographers Association. 37–46. doi: 10.1002/sono.12058

Mengersen, KL. (2016). How to be Bayesian using informative priors to improve decision making. 2016 Forum Mathematics-for-Industry (FMfI) .

Mengersen, KL. (2016). Building Bridges: New Bayesian Insights into Old Problems. The 4th Institute of Mathematical Statistics Asia Pacific Rim Meeting.

2015

Bednarz, T., Kim J., & Mengersen KL. (2015). Immersive Mixed Reality environments as modern interactive visualisation platforms. Bayes on the Beach.

Cook, A., Wu P., & Mengersen KL. (2015). Machine learning and visual analytics for consulting business decision support. 2015 Big Data Visual Analytics (BDVA). doi: 10.1109/BDVA.2015.7314299

Mengersen, KL., Taylor J., O'Keefe C., George A., & Bednarz T. (2015). Big data analytics as a tool to gain new insights. eResearch Australasia 2015.

Journal Articles

Submitted

Warne, D. J., Crossman K. A., Jin W., Mengersen KL., Osborne K., Simpson M. J., et al. (Submitted). Identification and modelling of two-phase recovery in hard corals across the Great Barrier Reef. Journal of Applied Ecology.

2021

Santos‐Fernandez, E., Peterson E. E., Vercelloni J., Rushworth E., & Mengersen KL. (2021). Correcting misclassification errors in crowdsourced ecological data: A Bayesian perspective. Journal of the Royal Statistical Society: Series C (Applied Statistics). 70(1), 147 - 173. doi: 10.1111/rssc.12453

Li, C., Xie H-B., Fan X., Da Xu R. Yi, Van Huffel S., & Mengersen KL. (2021). Kernelized Sparse Bayesian Matrix Factorization. IEEE Transactions on Neural Networks and Learning Systems. 32(1), 391 - 404. doi: 10.1109/TNNLS.2020.2978761

2020

Moores, M., Nicholls G., Pettitt A., & Mengersen KL. (2020). Scalable Bayesian Inference for the Inverse Temperature of a Hidden Potts Model. Bayesian Analysis. 15(1), 1-27. doi: 10.1214/18-BA1130

Burrage, K., Burrage P., Davis J., Bednarz T., Kim J., Vercelloni J., et al. (2020). A stochastic model of jaguar abundance in the Peruvian Amazon under climate variation scenarios. Ecology and Evolution. 10(19), 10829 - 10850. doi: 10.1002/ece3.6740

Warne, D. J., Ebert A., Drovandi C., Hu W., Mira A., & Mengersen KL. (2020). Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic. BMC Public Health. 20, 1868. doi: 10.1186/s12889-020-09972-z

Aswi, A.., Cramb S., Duncan E., Hu W., White G., & Mengersen KL. (2020). Climate variability and dengue fever in Makassar, Indonesia: Bayesian spatio-temporal modelling. Spatial and Spatio-temporal Epidemiology. 33, 100335. doi: 10.1016/j.sste.2020.100335

Perez, J. Rodriguez, Leigh C., Liquet B., Kermorvant C., Peterson E., Sous D., et al. (2020). Detecting technical anomalies in high-frequency water-quality data using Artificial Neural Networks. Environmental Science & Technology. 54(21), 13719-13730. doi: 10.1021/acs.est.0c04069

Aswi, A.., Cramb S., Duncan E., Hu W., White G., & Mengersen KL. (2020). Bayesian spatial survival models for hospitalisation of dengue: A case study of Wahidin hospital in Makassar, Indonesia. International Journal of Environmental Research and Public Health. 17(3), 878. doi: 10.3390/ijerph17030878

Vercelloni, J., Liquet B., Kennedy E. V., González‐Rivero M., M. Caley J., Peterson E. E., et al. (2020). Forecasting intensifying disturbance effects on coral reefs. Global Change Biology. 26(5), 2785 - 2797. doi: 10.1111/gcb.15059

Peterson, E. E., Santos-Fernández E., Chen C., Clifford S., Vercelloni J., Pearse A., et al. (2020). Monitoring through many eyes: Integrating disparate datasets to improve monitoring of the Great Barrier Reef. Environmental Modelling & Software. 124, 104557. doi: 10.1016/j.envsoft.2019.104557

Warne, D., Ebert A., Drovandi C., Hu W., Mira A., & Mengersen KL. (2020). Hindsight is 2020 vision: a characterisation of the global response to the COVID-19 pandemic. MedRxiv. doi: 10.1101/2020.04.30.20085662

Li, C., Xie H-B., Mengersen KL., Fan X., Da Xu R. Yi, Sisson S., et al. (2020). Bayesian Nonnegative Matrix Factorization With Dirichlet Process Mixtures. IEEE Transactions on Signal Processing. 68, 3860 - 3870. doi: 10.1109/TSP.2020.3003120

A. Farr, C., Mengersen KL., Ruggeri F., Simpson D., Wu P., & Yarlagadda P. (2020). Combining Opinions for Use in Bayesian Networks: A Measurement Error Approach. International Statistical Review. 88(2), 335-353. doi: 10.1111/insr.12350

Gilholm, P., Mengersen KL., & Thompson H. (2020). Identifying latent subgroups of children with developmental delay using Bayesian sequential updating and Dirichlet process mixture modelling. (Kwok, M. Ki, Ed.).PLOS ONE. 15(6), e0233542. doi: 10.1371/journal.pone.0233542

Jahan, F., Duncan E. W., Cramb S., Baade P. D., & Mengersen KL. (2020). Augmenting disease maps: a Bayesian meta-analysis approach. Royal Society Open Science. 7(8), 192151. doi: 10.1098/rsos.192151

Holloway-Brown, J., Helmstedt K. J., & Mengersen KL. (2020). Stochastic spatial random forest (SS-RF) for interpolating probabilities of missing land cover data. Journal of Big Data. 7(1), 55. doi: 10.1186/s40537-020-00331-8

Adams, M. P., Sisson S., Helmstedt K. J., Baker C. M., Holden M. H., Plein M., et al. (2020). Informing management decisions for ecological networks, using dynamic models calibrated to noisy time‐series data. Ecology Letters. 23(4), 607-619. doi: 10.1111/ele.13465

Cespedes, M. I., McGree J. M., Drovandi C. C., Mengersen KL., Fripp J., & Doecke J. D. (2020). Relative rate of change in cognitive score network dynamics via Bayesian hierarchical models reveal spatial patterns of neurodegeneration. Statistics in Medicine. 39(21), 2695 - 2713. doi: 10.1002/sim.8568

2019

Thomas, A., White N. M., Toms L-M. Leontjew, & Mengersen KL. (2019). Application of ensemble methods to analyse the decline of organochlorine pesticides in relation to the interactions between age, gender and time. (Garrett, T. J., Ed.).PLOS ONE. 14(11), e0223956. doi: 10.1371/journal.pone.0223956

Colin, B., & Mengersen KL. (2019). Estimating Spatial and Temporal Trends in Environmental Indices Based on Satellite Data: A Two-Step Approach. Sensors. 19(2), 361. doi: 10.3390/s19020361

Holloway, J., Helmstedt K. J., Mengersen KL., & Schmidt M. (2019). A Decision Tree Approach for Spatially Interpolating Missing Land Cover Data and Classifying Satellite Images. Remote Sensing. 11(15), 1796. doi: 10.3390/rs11151796

Wu, P. Pao- Yen, Sterkenburg N., Everett K., Chapman D. W., White N., & Mengersen KL. (2019). Predicting fatigue using countermovement jump force-time signatures: PCA can distinguish neuromuscular versus metabolic fatigue. PLoS ONE. 14(7), e0219295. doi: 10.1371/journal.pone.0219295

Benton, M. C., Lea R. A., Macartney-Coxson D., Sutherland H. G., White N., Kennedy D., et al. (2019). Genome-wide allele-specific methylation is enriched at gene regulatory regions in a multi-generation pedigree from the Norfolk Island isolate. Epigenetics & Chromatin. 12, 60. doi: 10.1186/s13072-019-0304-7

Sutton, M., Mengersen KL., & Liquet B.. (2019). [HDDA] sparse subspace constrained partial least squares. Journal of Statistical Computation and Simulation. 89(6), 1005-1019. doi: 10.1080/00949655.2018.1555830

Duncan, E. W., Cramb S., Aitken J. F., Mengersen KL., & Baade P. D. (2019). Development of the Australian Cancer Atlas: spatial modelling, visualisation, and reporting of estimates. International Journal of Health Geographics. 18(1), 21. doi: 10.1186/s12942-019-0185-9

Santos-Fernández, E., Wu P., & Mengersen KL. (2019). Bayesian statistics meets sports: a comprehensive review. Journal of Quantitative Analysis in Sports. 15(4), 289 - 312. doi: 10.1515/jqas-2018-0106

M. MacNeil, A., Mellin C., Matthews S., Wolff N. H., McClanahan T. R., Devlin M., et al. (2019). Water quality mediates resilience on the Great Barrier Reef. Nature Ecology & Evolution. 3(4), 620 - 627. doi: 10.1038/s41559-019-0832-3

Zhang, Y., Bambrick H., Mengersen KL., Tong S., Feng L., Zhang L., et al. (2019). Resurgence of Pertussis Infections in Shandong, China: Space-Time Cluster and Trend Analysis. The American Journal of Tropical Medicine and Hygiene. 100(6), 1342 - 1354. doi: 10.4269/ajtmh.19-0013

Talagala, P. Dilini, Hyndman R. J., Leigh C., Mengersen KL., & Smith-Miles K. (2019). A feature‐based procedure for detecting technical outliers in water‐quality data from in situ sensors. Water Resources Research. 55(11), 8547-8568. doi: 10.1029/2019WR024906

Kennedy, D. W., White N. M., Benton M. C., Lea R. A., & Mengersen KL. (2019). Cell-type specific analysis of heterogeneous methylation signal using a Bayesian model-based approach. bioRxiv. 682070. doi: 10.1101/682070

Leigh, C., Alsibai O., Hyndman R. J., Kandanaarachchi S., King O. C., McGree J. M., et al. (2019). A framework for automated anomaly detection in high frequency water-quality data from in situ sensors. Science of The Total Environment. 664, 885 - 898. doi: 10.1016/j.scitotenv.2019.02.085

Wu, P. Pao‐Yen, Mengersen KL., M. Caley J., McMahon K., Rasheed M. A., Kendrick G. A., et al. (2019). Analysing the dynamics and relative influence of variables affecting ecosystem responses using functional PCA and boosted regression trees: A seagrass case study. Methods in Ecology and Evolution. 10(10), 1723 - 1733. doi: 10.1111/2041-210X.13269

Tierney, N. J., Mira A., H. Reinhold J., Arbia G., Clifford S., Auricchio A., et al. (2019). Evaluating health facility access using Bayesian spatial models and location analysis methods. (Shah, T. Ikram, Ed.).PLOS ONE. 14(8), e0218310. doi: 10.1371/journal.pone.0218310

Ullah, I., & Mengersen KL. (2019). Bayesian mixture models and their Big Data implementations with application to invasive species presence-only data. Journal of Big Data. 6(1), 29. doi: 10.1186/s40537-019-0188-1

Li, C., Xie H-B., Fan X., Da Xu R. Yi, Van Huffel S., Sisson S., et al. (2019). Image Denoising Based on Nonlocal Bayesian Singular Value Thresholding and Stein’s Unbiased Risk Estimator. IEEE Transactions on Image Processing. 28(10), 4899 - 4911. doi: 10.1109/TIP.2019.2912292

Aswi, A.., Cramb S., Moraga P., & Mengersen KL. (2019). Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review. Epidemiology & Infection. 147, e33. doi: 10.1017/S0950268818002807

Leigh, C., Kandanaarachchi S., McGree J. M., Hyndman R. J., Alsibai O., Mengersen KL., et al. (2019). Predicting sediment and nutrient concentrations from high-frequency water-quality data. PLOS ONE. 14(8), e0215503. doi: 10.1371/journal.pone.0215503

2018

Nott, D. J., Drovandi C. C., Mengersen KL., & Evans M. (2018). Approximation of Bayesian Predictive p -Values with Regression ABC. Bayesian Analysis. 13(1), 59 - 83. doi: 10.1214/16-BA1033

Wu, P., M. Caley J., Kendrick G. A., McMahon K., & Mengersen KL. (2018). Dynamic Bayesian network inferencing for non-homogeneous complex systems. Journal of the Royal Statistical Society: Series C (Applied Statistics). 67(2), 417-434. doi: 10.1111/rssc.12228

Cespedes, M. Ines, McGree J. M., Drovandi C. C., Mengersen KL., Doecke J. D., & Fripp J. (2018). AGE DEPENDENT BAYESIAN NETWORKS REVEAL SPATIO-TEMPORAL PATTERNS OF NEURODEGENERATION IN HEALTHY AGEING AND ALZHEIMER’S DISEASE. Alzheimer's & Dementia. 14(7), P1228. doi: 10.1016/j.jalz.2018.06.1727

Wang, X., Nott D. J., Drovandi C. C., Mengersen KL., & Evans M. (2018). Using History Matching for Prior Choice. Technometrics. 60(4), 445 - 460. doi: 10.1080/00401706.2017.1421587

Colin, B., Schmidt M., Clifford S., Woodley A., & Mengersen KL. (2018). Influence of Spatial Aggregation on Prediction Accuracy of Green Vegetation Using Boosted Regression Trees. Remote Sensing. 10(8), 1260. doi: 10.3390/rs10081260

Cespedes, M. I., McGree J., Drovandi C. C., Mengersen KL., Doecke J. D., Fripp J., et al. (2018). An efficient algorithm for estimating brain covariance networks. PLOS ONE. 13(7), e0198583. doi: 10.1371/journal.pone.0198583

Kennedy, DW., White N., Benton M. C., Fox A., Scott R. J., Griffiths L. R., et al. (2018). Critical evaluation of linear regression models for cell-subtype specific methylation signal from mixed blood cell DNA. (Tost, J., Ed.).PLOS ONE. 13(12), e0208915. doi: 10.1371/journal.pone.0208915

Hargrave, C., Deegan T., Bednarz T., Poulsen M., Harden F., & Mengersen KL. (2018). An image‐guided radiotherapy decision support framework incorporating a Bayesian network and visualization tool. Medical Physics. 45(7), 2884 - 2897. doi: 10.1002/mp.12979

Holloway, J., & Mengersen KL. (2018). Statistical Machine Learning Methods and Remote Sensing for Sustainable Development Goals: A Review. Remote Sensing. 10(9), 1365. doi: 10.3390/rs10091365

Sequeira, A.. M. M., Rodríguez J.. P., Eguíluz V.. M., Harcourt R.., Hindell M.., Sims D.. W., et al. (2018). Convergence of marine megafauna movement patterns in coastal and open oceans. Proceedings of the National Academy of Sciences. 115(12), 3072-3077. doi: 10.1073/pnas.1716137115

Wu, P., McMahon K., Rasheed M. A., Kendrick G. A., York P. H., Chartrand K., et al. (2018). Managing seagrass resilience under cumulative dredging affecting light: Predicting risk using dynamic Bayesian networks. Journal of Applied Ecology. 55(3), 1339-1350. doi: 10.1111/1365-2664.13037

Hargrave, C., Deegan T., Poulsen M., Bednarz T., Harden F., & Mengersen KL. (2018). A feature alignment score for online cone‐beam. Medical Physics. 45(7), 2898 - 2911. doi: 10.1002/mp.12980

Vercelloni, J., Clifford S., M. Caley J., Pearse A. R., Brown R., James A., et al. (2018). Using virtual reality to estimate aesthetic values of coral reefs. Royal Society Open Science. 5(4), 172226. doi: 10.1098/rsos.172226

Sequeira, A. M. M., Bouchet P. J., Yates K. L., Mengersen KL., & M. Caley J. (2018). Transferring biodiversity models for conservation: Opportunities and challenges. (McPherson, J., Ed.).Methods in Ecology and Evolution. 9(5), 1250-1264. doi: 10.1111/2041-210X.12998

Leigh, C., Kandanaarachchi S., McGree J., Hyndman R. J., Alsibai O., Mengersen KL., et al. (2018). Predicting sediment and nutrient concentrations in rivers using high frequency water quality surrogates.

Xie, H-B., Sivakumar B., Boonstra T. W., & Mengersen KL. (2018). Fuzzy Entropy and Its Application for Enhanced Subspace Filtering. IEEE Transactions on Fuzzy Systems. 26(4), 1970 - 1982. doi: 10.1109/TFUZZ.2017.2756829

Leigh, C., Alsibai O., Hyndman R. J., Kandanaarachchi S., King O. C., McGree J., et al. (2018). A framework for automated anomaly detection in high frequency water-quality data from in situ sensors. arXiv preprints.

2017

Fraser, M. W., Short J., Kendrick G., McLean D., Keesing J., Byrne M., et al. (2017). Effects of dredging on critical ecological processes for marine invertebrates, seagrasses and macroalgae, and the potential for management with environmental windows using Western Australia as a case study. Ecological Indicators. 78, 229 - 242. doi: 10.1016/j.ecolind.2017.03.026

Moraga, P., Cramb S., Mengersen KL., & Pagano M. (2017). A geostatistical model for combined analysis of point-level and area-level data using INLA and SPDE. Spatial Statistics. 21, 27 - 41. doi: 10.1016/j.spasta.2017.04.006

White, N., Benton M., Kennedy DW., Fox A., Griffiths L., Lea R., et al. (2017). Accounting for cell lineage and sex effects in the identification of cell-specific DNA methylation using a Bayesian model selection algorithm. PLoS ONE. 12, Article number: e0182455. doi: 10.1371/journal.pone.0182455

Baker, J., White N., Mengersen KL., Rolfe M., & Morgan G. G. (2017). Joint modelling of potentially avoidable hospitalisation for five diseases accounting for spatiotemporal effects: A case study in New South Wales, Australia. (Ali, M., Ed.).PLOS ONE. 12(8), e0183653. doi: 10.1371/journal.pone.0183653

Zhang, Y., Milinovich G., Xu Z., Bambrick H., Mengersen KL., Tong S., et al. (2017). Monitoring pertussis infections using internet search queries. Scientific Reports. 7, Article number–10437. doi: 10.1038/s41598-017-11195-z

Cespedes, M. I., Fripp J., McGree J., Drovandi C. C., Mengersen KL., & Doecke J. D. (2017). Comparisons of neurodegeneration over time between healthy ageing and Alzheimer's disease cohorts via Bayesian inference. BMJ Open. 7(2), 12174-12174. doi: 10.1136/bmjopen-2016-012174

Drovandi, C. C., Holmes C. C., McGree J., Mengersen KL., Richardson S., & Ryan E. G. (2017). Principles of Experimental Design for Big Data Analysis. Statistical Science. 32(3), 385 - 404. doi: 10.1214/16-STS604

Chen, C. Chia- Ming, Keith J.M.., & Mengersen KL. (2017). Accurate phenotyping: Reconciling approaches through Bayesian model averaging. PLoS ONE. 12(4), e0176136. doi: 10.1371/journal.pone.0176136

Johnson, S., Logan M., Fox D., Kirkwood J., Pinto U., & Mengersen KL. (2017). Environmental decision-making using Bayesian networks: Creating an environmental report card. Applied Stochastic Models in Business and Industry. doi: 10.1002/asmb.2190

Wu, P., Mengersen KL., McMahon K., Kendrick G. A., Chartrand K., York P. H., et al. (2017). Timing anthropogenic stressors to mitigate their impact on marine ecosystem resilience. Nature Communications. 8(1), doi: 10.1038/s41467-017-01306-9

Vercelloni, J., M. Caley J., & Mengersen KL. (2017). Crown-of-thorns starfish undermine the resilience of coral populations on the Great Barrier Reef. Global Ecology and Biogeography. 26(7), 846 - 853. doi: 10.1111/geb.12590

Mengersen, KL., Peterson E., Clifford S., Ye N., Kim J., Bednarz T., et al. (2017). Modelling imperfect presence data obtained by citizen science. Environmetrics. 28(5), e2446. doi: 10.1002/env.2446

Thomas, A., Toms L-M. L., Harden F. A., Hobson P., White N., Mengersen KL., et al. (2017). Concentrations of organochlorine pesticides in pooled human serum by age and gender. Environmental Research. 154, 10-18. doi: 10.1016/j.envres.2016.12.009

Chen, C.C.-M.., Drovandi C. C., Keith J.M.., Anthony K., M. Caley J., & Mengersen KL. (2017). Bayesian semi-individual based model with approximate Bayesian computation for parameters calibration: Modelling Crown-of-Thorns populations on the Great Barrier Reef. Ecological Modelling. 364, 113 - 123. doi: 10.1016/j.ecolmodel.2017.09.006

Sommerfeld, J., Buys L., Mengersen KL., & Vine D. (2017). Influence of demographic variables on uptake of domestic solar photovoltaic technology. Renewable and Sustainable Energy Reviews. 67, 315–323. doi: 10.1016/j.rser.2016.09.009

Cramb, S., Moraga P., Mengersen KL., & Baade P. D. (2017). Spatial variation in cancer incidence and survival over time across Queensland, Australia. Spatial and Spatio-temporal Epidemiology. 23, 59 - 67. doi: 10.1016/j.sste.2017.09.002

Colin, B., Clifford S., Wu P., Rathmanner S., & Mengersen KL. (2017). Using Boosted Regression Trees and Remotely Sensed Data to Drive Decision-Making. Open Journal of Statistics. 07(05), 859 - 875. doi: 10.4236/ojs.2017.75061

Vercelloni, J., Mengersen KL., Ruggeri F., & M. Caley J. (2017). Improved Coral Population Estimation Reveals Trends at Multiple Scales on Australia’s Great Barrier Reef. Ecosystems. 20(7), 1337 - 1350. doi: 10.1007/s10021-017-0115-2

Liquet, B.., Mengersen KL., Pettitt A.N., & Sutton M. (2017). Bayesian Variable Selection Regression of Multivariate Responses for Group Data. Bayesian Analysis. 12(4), 1039 - 1067. doi: 10.1214/17-BA1081

Chen, CC-M., Bourne D. G., Drovandi C. C., Mengersen KL., Willis B. L., M. Caley J., et al. (2017). Modelling environmental drivers of black band disease outbreaks in populations of foliose corals in the genus Montipora. PeerJ. 5, e3438. doi: 10.7717/peerj.3438

2016

Beaudequin, D., Harden F., Roiko A., & Mengersen KL. (2016). Utility of Bayesian networks in QMRA-based evaluation of risk reduction options for recycled water. Science of The Total Environment. 541, 1393-1409. doi: 10.1016/j.scitotenv.2015.10.030

Reddan, T., Corness J., Mengersen KL., & Harden F. (2016). Ultrasound of paediatric appendicitis and its secondary sonographic signs: Providing a more meaningful finding. Journal of Medical Radiation Sciences. 63(1), 59-66. doi: 10.1002/jmrs.154

Kang, S. Y., Cramb S., White N., Ball S. J., & Mengersen KL. (2016). Making the most of spatial information in health: a tutorial in Bayesian disease mapping for areal data. Geospatial Health. 11(2), doi: 10.4081/gh.2016.428

Hsieh, J. C. - F., Cramb S., McGree J., Dunn N. A. M., Baade P. D., & Mengersen KL. (2016). Spatially varying coefficient inequalities: Evaluating how the impact of patient characteristics on breast cancer survival varies by location. PLOS ONE. 11(5), doi: 10.1371/journal.pone.0155086

Wells, J. A., Wilson K. A., Abram N., Nunn M., Gaveau D. L. A., Runting R. K., et al. (2016). Rising floodwaters: Mapping impacts and perceptions of flooding in Indonesian Borneo. Environmental Research Letters. 11(6), doi: 10.1088/1748-9326/11/6/064016

Ashcroft, M. B., Casanova-Katny A., Mengersen KL., Rosenstiel T. N. A., Turnbull J. D., Wasley J., et al. (2016). Bayesian methods for comparing species physiological and ecological response curves. Ecological Informatics. 34, 35-43. doi: 10.1016/j.ecoinf.2016.03.001

Brown, R., Bruza P., Heard W., Mengersen KL., & Murray J. (2016). On the (virtual) getting of wisdom: Immersive 3D interfaces for eliciting spatial information from experts. Spatial Statistics. 18(Part A), 318-331. doi: 10.1016/j.spasta.2016.07.001

Cramb, S., Mengersen KL., & Baade P. D. (2016). Spatio-temporal relative survival of breast and colorectal cancer in Queensland, Australia 2001–2011. Spatial and Spatio-temporal Epidemiology. 19, 103-114. doi: 10.1016/j.sste.2016.08.002

Hargrave, C., Mason N., Guidi R., Miller J-A., Becker J., Moores M., et al. (2016). Automated replication of cone beam CT-guided treatments in the Pinnacle treatment planning system for adaptive radiotherapy. Journal of Medical Radiation Sciences. 63(1), 48-58. doi: 10.1002/jmrs.141

Tuyl, F., Gerlach R., & Mengersen KL. (2016). Consensus priors for multinomial and binomial ratios. Journal of Statistical Theory and Practice. 10(4), 736-754. doi: 10.1080/15598608.2016.1219684

Fitzpatrick, B., Lamb D. W., & Mengersen KL. (2016). Ultrahigh dimensional variable selection for interpolation of point referenced spatial data: A digital soil mapping case study. PLOS ONE. 11(9), doi: 10.1371/journal.pone.0162489

Thomas, R., Walland M., Thomas A., & Mengersen KL. (2016). Lowering of intraocular pressure after phacoemulsification in primary open-angle and angle-closure glaucoma. Asia-Pacific Journal of Ophthalmology. 5(1), 79-84. doi: 10.1097/APO.0000000000000174

Cramb, S., Mengersen KL., Lambert P. C., Ryan L. M., & Baade P. D. (2016). A flexible parametric approach to examining spatial variation in relative survival. Statistics in Medicine. 35(29), 5448-5463. doi: 10.1002/sim.7071

Xie, H., Dokos S., Sivakumar B., & Mengersen KL. (2016). Symplectic geometry spectrum regression for prediction of noisy time series. Physical Review E. 93(5), doi: 10.1103/PhysRevE.93.052217

Huang, X., Clements A. C. A., Williams G., Mengersen KL., Tong S., & Hu W. (2016). Bayesian estimation of the dynamics of pandemic (H1N1) 2009 influenza transmission in Queensland: A space–time SIR-based model. Environmental Research. 146, 308–314. doi: 10.1016/j.envres.2016.01.013

Gonzalez, L., Montes G., Puig E., Johnson S., Mengersen KL., & Gaston K. (2016). Unmanned Aerial Vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation. Sensors. 16(1), doi: 10.3390/s16010097

Reddan, T., Corness J., Mengersen KL., & Harden F. (2016). Sonographic diagnosis of acute appendicitis in children: A 3-year retrospective. Sonography. 3(3), 87-94. doi: 10.1002/sono.12068

Thomas, R., Mengersen KL., Thomas A., & Walland M. J. (2016). Association between location of laser iridotomy and frequency of visual symptoms: a Bayesian learning analysis. Clinical & Experimental Ophthalmology. 44(3), 215-217. doi: 10.1111/ceo.12667

Arbel, J., Mengersen KL., & Rousseau J. (2016). Bayesian nonparametric dependent model for partially replicated data: The influence of fuel spills on species diversity. The Annals of Applied Statistics. 10(3), 1496 - 1516. doi: 10.1214/16-AOAS944

Xie, G., Roiko A., Stratton H., Lemckert C., Dunn P. K., & Mengersen KL. (2016). A generalized QMRA beta-Poisson dose-response model. Risk Analysis. 36(10), 1948-1958. doi: 10.1111/risa.12561

Duncan, EW., White N., & Mengersen KL. (2016). Bayesian spatiotemporal modelling for identifying unusual and unstable trends in mammography utilisation. BMJ Open. 6(5), doi: 10.1136/bmjopen-2015-010253

Kang, S. Y., McGree J., Drovandi C. C., M. Caley J., & Mengersen KL. (2016). Bayesian adaptive design: Improving the effectiveness of monitoring of the Great Barrier Reef. Ecological Applications. 26(8), 2637-2648. doi: 10.1002/eap.1409

Mengersen, KL., Drovandi C. C., Robert C. P., Pyne D. B., Gore C. J., & Chen C. W. S. (2016). Bayesian estimation of small effects in exercise and sports science. PLOS ONE. 11(4), 1-23. doi: 10.1371/journal.pone.0147311

Reddan, T., Corness J., Powell J., Harden F., & Mengersen KL. (2016). Stumped? It could be stump appendicitis. Sonography. doi: 10.1002/sono.12098

White, N., & Mengersen KL. (2016). Predicting health programme participation: A gravity-based, hierarchical modelling approach. Journal of the Royal Statistical Society: Series C (Applied Statistics). 65(1), 145-166. doi: 10.1111/rssc.12111

2015

Beaudequin, D., Harden F., Roiko A., Stratton H., Lemckert C., & Mengersen KL. (2015). Modelling microbial health risk of wastewater reuse: A systems perspective. Environment International. 84, 131-141. doi: 10.1016/j.envint.2015.08.001

Mengersen, KL., M. MacNeil A., & M. Caley J. (2015). The potential for meta-analysis to support decision analysis in ecology. Research Synthesis Methods. 6(2), 111-121. doi: 10.1002/jrsm.1105

Thomas, R., Mengersen KL., Thomas A., & Walland M. J. (2015). Looking deeper than (just) below the surface. Clinical & Experimental Ophthalmology. 43(5), 492-493. doi: 10.1111/ceo.12404

Herschtal, A., Marvelde L. Te, Mengersen KL., Foroudi F., Eade T., Pham D., et al. (2015). Sparing Healthy Tissue and Increasing Tumor Dose Using Bayesian Modeling of Geometric Uncertainties for Planning Target Volume Personalization. International Journal of Radiation Oncology*Biology*Physics. 92(2), 446-452. doi: 10.1016/j.ijrobp.2015.01.034

Arbel, J., King C.K., Raymond B., Winsley T., & Mengersen KL. (2015). Application of a Bayesian nonparametric model to derive toxicity estimates based on the response of Antarctic microbial communities to fuel-contaminated soil. Ecology and Evolution. 5(13), 2633-2645. doi: 10.1002/ece3.1493

Beaudequin, D., Harden F., Roiko A., Stratton H., Lemckert C., & Mengersen KL. (2015). Beyond QMRA: Modelling microbial health risk as a complex system using Bayesian networks. Environment International. 80, 8-18. doi: 10.1016/j.envint.2015.03.013

Lewis, J., Mengersen KL., Buys L., Vine D., Bell J., Morris P., et al. (2015). Systems Modelling of the Socio-Technical Aspects of Residential Electricity Use and Network Peak Demand. PLOS ONE. 10(7), doi: 10.1371/journal.pone.0134086

Cramb, S., Baade P. D., White N., Ryan L. M., & Mengersen KL. (2015). Inferring lung cancer risk factor patterns through joint Bayesian spatio-temporal analysis. Cancer Epidemiology. 39(3), 430-439. doi: 10.1016/j.canep.2015.03.001

Kang, S. Yun, McGree J., & Mengersen KL. (2015). Bayesian hierarchical models for analysing spatial point-based data at a grid level: A comparison of approaches. Environmental and Ecological Statistics. 2, 297–327. doi: 10.1007/s10651-014-0299-y

Pitchforth, J., Wu P., Fookes C., & Mengersen KL. (2015). Processing passengers efficiently: An analysis of airport processing times for international passengers. Journal of Air Transport Management. 49, 35-45. doi: 10.1016/j.jairtraman.2015.06.016

Hsieh, J. C. - F., Cramb S., McGree J., Dunn N., Baade P. D., & Mengersen KL. (2015). Does geographic location impact the survival differential between screen- and interval-detected breast cancers?. Stochastic Environmental Research and Risk Assessment. 30(1), 155-165. doi: 10.1007/s00477-015-1050-4

Abram, N., Meijaard E., Wells J. A., Ancrenaz M., Pellier AS., Runting R. K., et al. (2015). Mapping perceptions of species' threats and population trends to inform conservation efforts: the Bornean orangutan case study. Diversity and Distributions. 21(5), 487-499. doi: 10.1111/ddi.12286

Banu, S., Guo Y., Hu W., Dale P., Mackenzie J. S., Mengersen KL., et al. (2015). Impacts of El Niño Southern Oscillation and Indian Ocean Dipole on dengue incidence in Bangladesh. Scientific Reports. 5, 16105. doi: 10.1038/srep16105

Kang, S. Yun, McGree J., Baade P. D., & Mengersen KL. (2015). A Case Study for Modelling Cancer Incidence Using Bayesian Spatio-Temporal Models. Australian & New Zealand Journal of Statistics. 57(3), 325-345. doi: 10.1111/anzs.12127

Chakraborty, S., Mengersen KL., Fidge C., Ma L., Lassen D., & Shi Y. (2015). Multifaceted Modelling of Complex Business Enterprises. PLOS ONE. 10(8), doi: 10.1371/journal.pone.0134052

O’Leary, R. A., Low-Choy S., Fisher R., Mengersen KL., M. Caley J., & Alekseyenko A. V. (2015). Characterising Uncertainty in Expert Assessments: Encoding Heavily Skewed Judgements. PLOS ONE. 10(10), doi: 10.1371/journal.pone.0141697

Herschtal, A., Foroudi F., Kron T., & Mengersen KL. (2015). A Comparison of Bayesian Models of Heteroscedasticity in Nested Normal Data. Communications in Statistics - Simulation and Computation. 45(8), 2947-2964. doi: 10.1080/03610918.2014.936467

Fisher, R., O’Leary R. A., Low-Choy S., Mengersen KL., Knowlton N., Brainard R. E., et al. (2015). Species Richness on Coral Reefs and the Pursuit of Convergent Global Estimates. Current Biology. 25(4), 500-505. doi: 10.1016/j.cub.2014.12.022

Baker, J., White N., & Mengersen KL. (2015). Spatial modelling of type II diabetes outcomes: a systematic review of approaches used. Royal Society Open Science. 2(6), 140460. doi: 10.1098/rsos.140460

Alston, C.L.., Mengersen KL., & Pettitt A.N.. (2015). Case Studies in Bayesian Statistical Modelling and Analysis. The Quarterly Review of Biology. 90(3), 319 - 320. doi: 10.1086/682596

Hu, W., Zhang W., Huang X., Clements A., Mengersen KL., & Tong S. (2015). Weather variability and influenza A (H7N9) transmission in Shanghai, China: A Bayesian spatial analysis. Environmental Research. 136, 405-412. doi: 10.1016/j.envres.2014.07.033

Buys, L., Vine D., Ledwich G., Bell J., Mengersen KL., Morris P., et al. (2015). A Framework for Understanding and Generating Integrated Solutions for Residential Peak Energy Demand. PLOS ONE. 10(3), doi: 10.1371/journal.pone.0121195

Nakagawa, S., Poulin R., Mengersen KL., Reinhold K., Engqvist L., Lagisz M., et al. (2015). Meta-analysis of variation: ecological and evolutionary applications and beyond. Methods in Ecology and Evolution. 6(2), 143-152. doi: 10.1111/2041-210X.12309

Falk, M. G., Alston C. L., McGrory C. A., Clifford S., Heron E. A., Leonte D., et al. (2015). Recent Bayesian approaches for spatial analysis of 2-D images with application to environmental modelling. Environmental and Ecological Statistics. 22(3), 571-600. doi: 10.1007/s10651-015-0311-1

Wu, P., Fookes C., Pitchforth J., & Mengersen KL. (2015). A framework for model integration and holistic modelling of socio-technical systems. Decision Support Systems. 71, 14-27. doi: 10.1016/j.dss.2015.01.006

Hsieh, J. C. - F., Cramb S., McGree J., Dunn N. A. M., Baade P. D., & Mengersen KL. (2015). Geographic variation in the intended choice of adjuvant treatments for women diagnosed with screen-detected breast cancer in Queensland. BMC Public Health. 15(1), doi: 10.1186/s12889-015-2527-2

Brown, E., Owen R., Harden F., Mengersen KL., Oestreich K., Houghton W., et al. (2015). Predicting the need for adaptive radiotherapy in head and neck cancer. Radiotherapy and Oncology. 116(1), 57-63. doi: 10.1016/j.radonc.2015.06.025

Moores, M., Hargrave C., Deegan T., Poulsen M., Harden F., & Mengersen KL. (2015). An external field prior for the hidden Potts model with application to cone-beam computed tomography. Computational Statistics & Data Analysis. 86, 27-41. doi: 10.1016/j.csda.2014.12.001

Donald, M. R., Mengersen KL., Young R. R., & Reigosa M. (2015). A Four Dimensional Spatio-Temporal Analysis of an Agricultural Dataset. PLOS ONE. 10(10), doi: 10.1371/journal.pone.0141120

van Havre, Z., White N., Rousseau J., Mengersen KL., & Chen C. W. S. (2015). Overfitting Bayesian Mixture Models with an Unknown Number of Components. PLOS ONE. 10(7), doi: 10.1371/journal.pone.0131739

Herschtal, A., L Marvelde te., Mengersen KL., Hosseinifard Z., Foroudi F., Devereux T., et al. (2015). Calculating radiotherapy margins based on Bayesian modelling of patient specific random errors. Physics in Medicine and Biology. 60(5), 1793-1805. doi: 10.1088/0031-9155/60/5/1793

2014

Moores, M., Drovandi C. C., Mengersen KL., & Robert C. P. (2014). Pre-processing for approximate Bayesian computation in image analysis. Statistics and Computing. 25(1), 23-33. doi: 10.1007/s11222-014-9525-6

Buys, L., Mengersen KL., Johnson S., van Buuren N., & Chauvin A. (2014). Creating a Sustainability Scorecard as a predictive tool for measuring the complex social, economic and environmental impacts of industries, a case study : assessing the viability and sustainability of the dairy industry. Journal of Environmental Management. 133, 184–192. doi: 10.1016/j.jenvman.2013.12.013

Yu, W., Mengersen KL., Dale P., Ye X., Guo Y., Turner L., et al. (2014). Projecting Future Transmission of Malaria Under Climate Change Scenarios: Challenges and Research Needs. Critical Reviews in Environmental Science and Technology. 45(7), 777-811. doi: 10.1080/10643389.2013.852392

Wu, P., Pitchforth J., & Mengersen KL. (2014). A Hybrid Queue-based Bayesian Network framework for passenger facilitation modelling. Transportation Research Part C: Emerging Technologies. 46, 247-260. doi: 10.1016/j.trc.2014.05.005

Davis, J., Mengersen KL., Bennett S., & Mazerolle L. (2014). Viewing systematic reviews and meta-analysis in social research through different lenses. SpringerPlus. 3(1), 511. doi: 10.1186/2193-1801-3-511

Vercelloni, J., M. Caley J., Kayal M., Low-Choy S., Mengersen KL., & Deng Y. (2014). Understanding Uncertainties in Non-Linear Population Trajectories: A Bayesian Semi-Parametric Hierarchical Approach to Large-Scale Surveys of Coral Cover. PLOS ONE. 9(11), doi: 10.1371/journal.pone.0110968

Mellin, C., Mengersen KL., Bradshaw C. J. A., & M. Caley J. (2014). Generalizing the use of geographical weights in biodiversity modelling. Global Ecology and Biogeography. 23(11), 1314-1323. doi: 10.1111/geb.12203

Publicly available softwares

2017

Ebert, A., Wu P., & Mengersen KL. (2017). queuecomputer.

Technical reports and unrefereed outputs

Submitted

Ullah, I., Mengersen KL., Hyndman R. J., & McGree J. (Submitted). Detection of cybersecurity attacks through analysis of web browsing activities using principal component analysis.

Kennedy, DW., White N., Mengersen KL., & Lea R. (Submitted). Cell-type specific analysis of heterogeneous methylation signal using a Bayesian model-based approach.

2019

Cameron, J., Kennedy D., & Mengersen KL. (2019). Clustering and Peer grouping DEX organisations.

Cameron, J., Kennedy D., & Mengersen KL. (2019). Executive Summary of the Benchmarking Project.

Shausan, A.., Mengersen KL., Drovandi C.., & Aaskov J.. (2019). DARPA dengue project.

2018

Cramb, S., Duncan EW., Baade P., & Mengersen KL. (2018). Investigation of Bayesian spatial models.

Cramb, S., Duncan EW., Mengersen KL., & Baade PD. (2018). Australian Cancer Atlas: small-area incidence technical report.

2017

Ebert, A., Wu P., Mengersen KL., & Ruggeri F. (2017). Computationally Efficient Simulation of Queues: The R Package queuecomputer. arXiv.

2016

Johnson, S., Logan M., Fox D., & Mengersen KL. (2016). Provision of statistical support during the development of the Gladstone Harbour report card.

Roberts, JL., Cramb S., Baade P. D., & Mengersen KL. (2016). Grey Literature Review: Internet Published Cancer Maps.

Cook, A., & Mengersen KL. (2016). Statistical review and update of detection surveilance systems for non indigenous species of terrestial plants vertebrates and invertebrates on Barrow Island.

Kennedy, DW., White N., Lea R., Benton M., Mengersen KL., & Griffiths L. (2016). Evaluating linear regression routines for deconvolution of methylation signal from whole blood.

Xueou, W., Nott D. J., Drovandi C. C., Mengersen KL., & Evans M. (2016). Using history matching for prior choice.

Caley, P., Stanaway M., Goodwin M., Barry S., Taylor S., Bottrill M., et al. (2016). Review and redesign of the national bee pest surveilance program.

Vercelloni, J., Mengersen KL., Peterson E., Bednarz T., & Brown R. (2016). Monitoring Through Many Eyes:Spatially enabling people to protect the Great Barrier Reef.

McBain, M., Fitzpatrick B., Colin B., Gough P., Wu P., & Mengersen KL. (2016). Dashboard project for Australian Agricutural Company.

Roberts, JL., Cramb S., Baade P. D., & Mengersen KL. (2016). Communicating Statistical Outputs Through Health Maps.

van Havre, Z., Mengersen KL., & Kelly M. (2016). Estimation of Meat Quality indicators from photos.

Cramb, S., Duncan EW., White N., Baade P. D., & Mengersen KL. (2016). Spatial Modelling Methods.

Research links

ORCID

Contact

k.mengersen@qut.edu.au

+61 (07) 313 82063

Welcome to the ACEMS Reportal

All contributors should enter data on all of the Centre’s reportable key performance indicators (KPIs).

Professor Kerrie Mengersen

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ARC Centre of Excellence for Mathematical & Statistical Frontiers