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Dissanayake, T., Fernando T., Denman S., Sridharan S., & Fookes C. (2022).  Geometric Deep Learning for Subject-Independent Epileptic Seizure Prediction using Scalp EEG Signals. IEEE Journal of Biomedical and Health Informatics. 26(2), 527-538. doi: 10.1109/JBHI.2021.3100297
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Jiang, J., Wand M. P., & Bhaskaran A. (2022).  Usable and precise asymptotics for generalized linear mixed model analysis and design. Journal of the Royal Statistical Society, Series B. 84(1), 55-82. doi: 10.1111/rssb.12473
Kandanaarachchi, S., & Hyndman R. J. (2022).  Leave-one-out kernel density estimates for outlier detection. Journal of Computational and Graphical Statistics. 31(2), 586-599. doi: 10.1080/10618600.2021.2000425
Laa, U., Cook D., & Lee S. (2022).  Burning Sage: Reversing the Curse of Dimensionality in the Visualization of High-Dimensional Data. Journal of Computational and Graphical Statistics. 31(1), 40-49. doi: 10.1080/10618600.2021.1963264
Rostami-Tabar, B., Ali M. M., Hong T., Hyndman R. J., Porter M. D., & Syntetos A. (2022).  Forecasting for social good. International Journal of Forecasting. 38(3), 1245-1257. doi: 10.1016/j.ijforecast.2021.02.010
Aswi, A., Sukarna, Cramb S., & Mengersen K. (2021).  Effects of Climatic Factors on Dengue Incidence: A Comparison of Bayesian Spatio-Temporal Models. Journal of Physics: Conference Series. 1863(1), 012050. doi: 10.1088/1742-6596/1863/1/012050
Aswi, A., Cramb S., Duncan E., & Mengersen K. (2021).  Detecting Spatial Autocorrelation for a Small Number of Areas: a practical example. Journal of Physics: Conference Series. 1899(1), 012098. doi: 10.1088/1742-6596/1899/1/012098
Ben Taieb, S., Taylor J. W., & Hyndman R. J. (2021).  Hierarchical Probabilistic Forecasting of Electricity Demand With Smart Meter Data. Journal of the American Statistical Association. 116(533), 27-43. doi: 10.1080/01621459.2020.1736081
Cui, T., & Zahm O. (2021).  Data-free likelihood-informed dimension reduction of Bayesian inverse problems. Inverse Problems. 37(4), 045009. doi: 10.1088/1361-6420/abeafb
Dawkins, L. C., Williamson D. B., Mengersen K. L., Morawska L., Jayaratne R., & Shaddick G. (2021).  Where Is the Clean Air? A Bayesian Decision Framework for Personalised Cyclist Route Selection Using R-INLA. Bayesian Analysis. 16(1), 61-91. doi: 10.1214/19-BA1193
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Eckert, F., Hyndman R. J., & Panagiotelis A. (2021).  Forecasting Swiss Exports using Bayesian Forecast Reconciliation. European Journal of Operational Research. 291(2), 693-710. doi: 10.1016/j.ejor.2020.09.046
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Li, H., & Hyndman R. J. (2021).  Assessing mortality inequality in the U.S.: What can be said about the future?. Insurance: Mathematics and Economics. 99, 152-162. doi: 10.1016/j.insmatheco.2021.03.014
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Miller, M. E., Motti C. A., Menéndez P., & Kroon F. J. (2021).  Efficacy of Microplastic Separation Techniques on Seawater Samples: Testing Accuracy Using High-Density Polyethylene. The Biological Bulletin. 240(1), 52 - 66. doi: 10.1086/710755
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Spiliotis, E., Abolghasemi M., Hyndman R. J., Petropoulos F., & Assimakopoulos V. (2021).  Hierarchical forecast reconciliation with machine learning. Applied Soft Computing. 112, 107756. doi: 10.1016/j.asoc.2021.107756
Thamrin, S. Astuti, Aswi, Ansariadi, Jaya A. Kresna, & Mengersen K. (2021).  Bayesian spatial survival modelling for dengue fever in Makassar, Indonesia. Gaceta Sanitaria. 35(S1), S59 - S63. doi: 10.1016/j.gaceta.2020.12.017
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Costa, A. Goncalves, Nielsen T., Dal Grande E., Tuke J., & Hazel S. (2020).  Regulatory Compliance in Online Dog Advertisements in Australia. Animals. 10(3), 425. doi: 10.3390/ani10030425
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Forbes, J., Cook D., & Hyndman R. J. (2020).  Spatial modelling of the two‐party preferred vote in Australian federal elections: 2001–2016. Australian & New Zealand Journal of Statistics. 62(2), 168 - 185. doi: 10.1111/anzs.12292
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Mitchell, D., Ye N., & De Sterck H. (2020).  Nesterov acceleration of alternating least squares for canonical tensor decomposition: Momentum step size selection and restart mechanisms. Numerical Linear Algebra with Applications. 27(4), e2297. doi: 10.1002/nla.2297
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Montero-Manso, P., Athanasopoulos G., Hyndman R. J., & Talagala T. S. (2020).  FFORMA: Feature-based forecast model averaging. International Journal of Forecasting. 36(1), 86 - 92. doi: 10.1016/j.ijforecast.2019.02.011
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Pham, D-T., Ristic R., Stockdale V. J., Jeffery D. W., Tuke J., & Wilkinson K. (2020).  Influence of partial dealcoholization on the composition and sensory properties of Cabernet Sauvignon wines. Food Chemistry. 325, 126869. doi: 10.1016/j.foodchem.2020.126869
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Wang, E., Cook D., & Hyndman R. J. (2020).  A New Tidy Data Structure to Support Exploration and Modeling of Temporal Data. Journal of Computational and Graphical Statistics. 29(3), 466-478. doi: 10.1080/10618600.2019.1695624
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Chakravorty, D., Banerjee K., Mapder T., & Saha S. (2019).  In silico modeling of phosphorylation dependent and independent c-Myc degradation. BMC Bioinformatics. 20, 230. doi: 10.1186/s12859-019-2846-x
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Mapder, T., Clifford S., Aaskov J., & Burrage K. (2019).  A population of bang-bang switches of defective interfering particles makes within-host dynamics of dengue virus controllable. (Papin, J. A., Ed.).PLOS Computational Biology. 15(11), e1006668. doi: 10.1371/journal.pcbi.1006668
Martin, G. M., McCabe B. P. M., Frazier D. T., Maneesoonthorn W., & Robert C. P. (2019).  Auxiliary Likelihood-Based Approximate Bayesian Computation in State Space Models. Journal of Computational and Graphical Statistics. 28(3), 508-522. doi: 10.1080/10618600.2018.1552154
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Padgham, M., Boeing G., Cooley D., Tierney N., Sumner M., Phan T. G., et al. (2019).  An Introduction to Software Tools, Data, and Services for Geospatial Analysis of Stroke Services. Frontiers in Neurology. 10, 743. doi: 10.3389/fneur.2019.00743
Parsons, S.., Fuller S.., Peterson E., & Doohan B.. (2019).  The sound of management: Acoustic monitoring for agricultural industries. Ecological Indicators. 96(Part 1), 739-746. doi: 10.1016/j.ecolind.2018.09.029
Pham, D-T., Stockdale V. J., Jeffery D. W., Tuke J., & Wilkinson K. L. (2019).  Investigating Alcohol Sweetspot Phenomena in Reduced Alcohol Red Wines. Foods. 8(10), 491. doi: 10.3390/foods8100491
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Sarker, C., Mejias L., Maire F., & Woodley A. (2019).  Flood Mapping with Convolutional Neural Networks Using Spatio-Contextual Pixel Information. Remote Sensing. 11(19), 2331. doi: 10.3390/rs11192331
Sharp, J. A., Browning A. P., Mapder T., Burrage K., & Simpson M. J. (2019).  Optimal control of acute myeloid leukaemia. Journal of Theoretical Biology. 470, 30–42. doi: 10.1016/j.jtbi.2019.03.006
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Wickramasuriya, S. L., Athanasopoulos G., & Hyndman R. J. (2019).  Optimal Forecast Reconciliation for Hierarchical and Grouped Time Series Through Trace Minimization. Journal of the American Statistical Association. 114(526), 804-819. doi: 10.1080/01621459.2018.1448825
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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
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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
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
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