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2018
Muszkiewicz, A., Liu X., Bueno-Orovio A., Lawson B. A. J., Burrage K., Casadei B., et al. (2018).  From ionic to cellular variability in human atrial myocytes: an integrative computational and experimental study. American Journal of Physiology-Heart and Circulatory Physiology. 314(5), H895 - H916.
Forrester, P., & Trinh A. K. (2018).  Functional form for the leading correction to the distribution of the largest eigenvalue in the GUE and LUE. Journal of Mathematical Physics. 59(5), 053302.
Lamers, J. (2018).  The Functional Method for the Domain-Wall Partition Function. Symmetry, Integrability and Geometry: Methods and Applications. 14,
Bednarz, T., Filonik D., Buchan A., & Ogden-Doyle L. (2018).  Future-mine VR as narrative decision making tool. the 24th ACM SymposiumProceedings of the 24th ACM Symposium on Virtual Reality Software and Technology - VRST '18. 1 - 2.
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.
Xie, H., & GUO TIANRUO. (2018).  Fuzzy entropy spectrum analysis for biomedical signals de-noising. 2018 IEEE EMBS International Conference on Biomedical & Health Informatics (BHI). 50-53.
Quiroz, M., Nott D., & Kohn R. (2018).  Gaussian variational approximation for high-dimensional state space models. 2nd International Conference on Econometrics and Statistics.
Quiroz, M., Nott D. J., & Kohn R. (2018).  Gaussian variational approximations for high-dimensional state space models.
Pham, T., & Wand M. (2018).  Generalised additive mixed models analysis via. Australian & New Zealand Journal of Statistics. 60(3), 279 - 300.
L'Ecuyer, P.., Botev Z. I., & Kroese D. (2018).  On A Generalized Splitting Method For Sampling From A Conditional Distribution. (Rabe, M.., Juan A.. A., Mustafee N.., Skoogh A.., Jain S.., & Johansson B.., Ed.).2018 Winter Simulation Conference.
Li, B., Zhang L., Wang Y-G., George A. W., Reverter A., & Li Y. (2018).  Genomic Prediction of Breeding Values Using a Subset of SNPs Identified by Three Machine Learning MethodsTable_1.pdf. Frontiers in Genetics. 9, 237.
Peterson, N., Hamilton G. S., Almany G., Pita J., Choat J. Howard, Hamilton R. J., et al. (2018).  Giant coral reef fishes display markedly different susceptibility to night spearfishing. Ecology and Evolution. 8(20), 10247 - 10256.
Wang, S., Roosta-Khorasani F., Xu P., & Mahoney M. W. (2018).  GIANT: Globally Improved Approximate Newton Method for Distributed Optimization. (Bengio, S.., Wallach H.., Larochelle H.., Grauman K.., Cesa-Bianchi N.., & Garnett R.., Ed.).Advances in Neural Information Processing Systems 31. 2338–2348.
Chattopadhyay, A., Blaszczyszyn B., & Keeler H. (2018).  Gibbsian On-Line Distributed Content Caching Strategy for Cellular Networks. IEEE Transactions on Wireless Communications. 17(2), 969-981.
Wand, M., & Yu J.F.C.. (2018).  glmmEP 1.0.
Whyte, J. M. (2018).  GLOBAL A PRIORI IDENTIFIABILITY OF MODELS OF FLOW-CELL OPTICAL BIOSENSOR EXPERIMENTS. Bulletin of the Australian Mathematical Society. 98(02), 350 - 352.
Datry, T.., Foulquier A.., Corti R.., von Schiller D.., Tockner K.., Mendoza-Lera C.., et al. (2018).  A global analysis of terrestrial plant litter dynamics in non-perennial waterways. Nature Geoscience. 11(7), 497-503.
Kylasa, S. B., Roosta-Khorasani F., Mahoney M. W., & Grama A. (2018).  GPU Accelerated Sub-Sampled Newton's Method. arXiv preprint arXiv:1802.09113.
Yin, D., Pananjady A., Lam M., Papailiopoulos D., Ramchandran K., & Bartlett P. (2018).  Gradient Diversity: a Key Ingredient for Scalable Distributed Learning. (Storkey, A., & Perez-Cruz F., Ed.).Proceedings of the 21st International Conference on Artificial Intelligence and Statistics. 84, 1998–2007.
Chen, Z., Hiki I., De Gier J., & Sasamoto T. (2018).  GUE distribution in a two-species exclusion processes. Integrable Systems 2018.
Dang, K.. D., Quiroz M., Kohn R., Tran M N., & Villani M. (2018).  Hamiltonian Monte Carlo with energy conserving subsampling. The 2nd International Conference on Econometrics and Statistics (EcoSta 2018).
[Anonymous] (2018).  Handbook of Approximate Bayesian Computation. (Sisson, S., Fan Y., & Beaumont M. A., Ed.).
Tune, P., Roughan M., & (2018).  Hierarchical Traffic Matrices: Axiomatic Foundations to Practical Traffic Matrix Synthesis. Asia-Pacific Signal and Information Processing Association (ASIPA) Conference. 1591-1600.
Nott, D. J., Ong V.. M. - H., Fan Y., & Sisson S. (2018).  High-Dimensional ABC. Handbook of Approximate Bayesian Computation. 211-242.
Hyndman, R. J. (2018).  High-dimensional time series analysis. Australasian Actuarial Education and Research Symposium.
Jang, J., & Bednarz T. (2018).  HoloSensor for smart home, health, entertainment. ACM SIGGRAPH 2018 Appy HourACM SIGGRAPH 2018 Appy Hour on - SIGGRAPH '18. 1 - 2.
Malek, A., & Bartlett P. L. (2018).  Horizon-Independent Minimax Linear Regression. (Bengio, S.., Wallach H.., Larochelle H.., Grauman K.., Cesa-Bianchi N.., & Garnett R.., Ed.).Advances in Neural Information Processing Systems 31. 5264–5273.
Taylor, P. (2018).  How does the Bitcoin blockchain work?. CEBRE Seminar, School of Biosciences, University of Melbourne.
Harezlak, J.., Ruppert D.., & Wand M. (2018).  HRW 1.0. Datasets, functions and scripts for semiparametric regression supporting Harezlak, Ruppert & Wand (2018). R package. https://CRAN.R-project.org/package=HRW.
Hyndman, R. J., Lee A., Wang E., & Wickramasuriya S. L. (2018).  hts: Hierarchical and Grouped Time Series.
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.
Ali, T. F., & Woodley A. (2018).  IGTMPP: A Hybrid Method to Predict Missing Pixels of Remote Sensing Images Using Geo-Temporal Properties. International Conference on Digital Image Computing: Techniques and Applications (DICTA) .
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.
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.
Borg, D.N.., Stewart I.B.., Costello J.T.., Drovandi C.C.., & Minett G.M.. (2018).  The impact of environmental temperature deception on perceived exertion during fixed-intensity exercise in the heat in trained-cyclists. Physiology & Behavior. 194, 333 - 340.
Ballard, P. (2018).  The impact of time-dependent transmission rate on the probability of epidemic fade-out. ANZIAM 2018.
Moka, S., & Juneja S. (2018).  Importance Sampling Based Unbiased Estimation for Hard-core Models. ACEMS Workshop on Multiscale Models 2018.
Ritt, M., & Costa A. M. (2018).  Improved integer programming models for simple assembly line balancing and related problems. International Transactions in Operational Research. 25(4), 1345 - 1359.
Drovandi, C. C., & Tran M N. (2018).  Improving the Efficiency of Fully Bayesian Optimal Design of Experiments Using Randomised Quasi-Monte Carlo. Bayesian Analysis. 13(1), 139–162.
Fransen, J., Bush S., Woodcock S., Novak A., Deprez D., Baxter-Jones A. D. G., et al. (2018).  Improving the Prediction of Maturity From Anthropometric Variables Using a Maturity Ratio. Pediatric Exercise Science. 30(2), 296 - 307.
Cramb, S. (2018).  In the pursuit of research funding. Joint International Society for Clinical Biostatistics and Australian Statistical Conference 2018. Early Career Researchers Day,
Khaled, M. A., Makdissi P., & Yazbeck M. (2018).  Income-related health transfers principles and orderings of joint distributions of income and health. Journal of Health Economics. 57, 315 - 331.
Chaudhuri, S., Frazier D. T., & Renault E. (2018).  Indirect Inference with endogenously missing exogenous variables. Journal of Econometrics. 205(1), 55 - 75.
Price, D. J., Bean N. G., Ross J.V., & Tuke J. (2018).  An induced natural selection heuristic for finding optimal Bayesian experimental designs. Computational Statistics & Data Analysis. 126, 112–124.
Yao, Z., Xu P., Roosta-Khorasani F., & Mahoney M. W. (2018).  Inexact non-convex Newton-type methods. arXiv preprint arXiv:1802.06925.
Beranger, B., Stephenson A. G., & Sisson S. (2018).  Inference for extremal-$t$ and skew-$t$ max-stable models in high dimensions. CMStatistics.
Rodrigues, G. S., Francis A. R., Sisson S., & Tanaka M. M. (2018).  Inferences on the Acquisition of Multi-Drug Resistance in Mycobacterium Tuberculosis Using Molecular Epidemiological Data. Handbook of Approximate Bayesian Computation. 481-511.
Pollett, P. (2018).  Infinite-patch metapopulation models: branching, convergence and chaos. 40th Conference on Stochastic Processes and their Applications.
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.
Muñoz, M. A., Villanova L., Baatar D., & Smith-Miles K. (2018).  Instance spaces for machine learning classification. Machine Learning. 107(1), 109 - 147.

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