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2019
Quill, R., Sharples J. J., Wagenbrenner N. S., Sidhu L. A., & Forthofer J. M. (2019).  Modeling Wind Direction Distributions Using a Diagnostic Model in the Context of Probabilistic Fire Spread Prediction. Frontiers in Mechanical Engineering. 5,
Niknami, B., & Taylor P. (2019).  Modelling Double Auctions with Dynamic Supply and Demand. The 20th INFORMS Applied Probability Society Conference.
James, S.., Bean N. G., & Tuke J.. (2019).  Modelling intensive care units using quasi-birth-and-death processes. The 10th International Conference on Matrix-Analytic Methods in Stochastic Models. 42.
James, S.., Bean N. G., & Tuke J.. (2019).  Modelling intensive care units using quasi-birth-and-death processes. YEQT XIII : "Data-Driven Analytics and Optimization for Stochastic Systems".
James, S.., Bean N. G., & Tuke J.. (2019).  Modelling intensive care units using quasi-birth-and-death processes. The 20th INFORMS Applied Probability Society Conference.
Cope, R. C., Mitchell L., Carlson S. J., Liu D., & Ross J. V. (2019).  Modelling of reporting behaviour in the FluTracking surveillance system. ANZIAM Conference 2019.
Cope, R. C., Mitchell L., Carlson S. J., Liu D., & Ross J. V. (2019).  Modelling of reporting behaviour in the FluTracking surveillance system. The 20th INFORMS Applied Probability Society Conference.
Garbali, A., & Wheeler M. (2019).  Modified Macdonald polynomials and integrability. arXiv. arXiv:1810.12905v2.
Meier, A., de Laat M., Pollitt C., Walsh D., McGree J., Reiche D. B., et al. (2019).  A “modified Obel” method for the severity scoring of (endocrinopathic) equine laminitis. PeerJ. 7, e7084.
Hyndman, R. J. (2019).  MonashEBSTemplates.
Laub, P. J., Salomone R., & Botev Z. I. (2019).  Monte Carlo estimation of the density of the sum of dependent random variables. Mathematics and Computers in Simulation. 161, 23-31.
Polishchuk, S., De Sterck H., & Cui T. (2019).  Multi-level and Multi-index Monte Carlo Discontinuous Galerkin Methods for Uncertainty Quantification of Nonlinear Hyperbolic Problems. SIAM Conference on Computational Science and Engineering (CSE19).
Cui, T., Detommaso G., & Scheichl R. (2019).  Multilevel Dimension-Independent Likelihood-Informed MCMC for Large-Scale Inverse problems. arXiv. arXiv:1910.12431v1.
Chen, C. (2019).   A Multi-Level Optimization Based Monte-Carlo Sampler for Large Scale Inverse Problems. SIAM CSE 2019.
Bednarz, T. (2019).  Multi-Modal Collaborative High-End Visualisation System.
Bednarz, T., & Bourke C. (2019).  Multi-modal High-end Visualisation System (HEVS).
McArthur, L., & Humphries M. A. (2019).  Multi-model mimicry for model selection according to generalised goodness-of-fit criteria. arXiv. arXiv:1911.09779v2.
Kieburg, M., Forrester P., & Ipsen J. R. (2019).  Multiplicative convolution of real asymmetric and real anti-symmetric matrices. Advances in Pure and Applied Mathematics. 10(4), 467 - 492.
Barbour, A.. D., & Xia A.. (2019).  Multivariate approximation in total variation using local dependence. Electronic Journal of Probability. 24, 27.
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.
Bourke, C., & Bednarz T. (2019).  Multi–Modal High–End Visualization System. The 17th International Conference on Virtual-Reality Continuum and its Applications in IndustryThe 17th International Conference on Virtual-Reality Continuum and its Applications in Industry on - VRCAI '19. 1 - 2.
Herath, S. (2019).  Name-like Numbers for Simulating Names in Entity Resolution. Data Science Down Under 2019.
Herath, S. (2019).  Name-like Numbers for Simulating Names in Entity Resolution. The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases.
Herath, S. (2019).  Name-like Numbers for Simulating Names in Entity Resolution. Young Statisticians Conference 2019.
Sonenberg, N., & Taylor P. (2019).  Networks of interacting stochastic fluid models with infinite and finite buffers. Queueing Systems. 92(3-4), 293 - 322.
CHARKHGARD, HADI., & ESHRAGH ALI. (2019).  A New Approach to Select the Best Subset of Predictors in Linear Regression Modelling: Bi-objective Mixed Integer Linear Programming. The ANZIAM Journal. 61(1), 64 - 75.
Wu, J., Cui Z., Chen Y., Kong D., & Wang Y-G. (2019).  A new hybrid model to predict the electrical load in five states of Australia. Energy. 166, 598 - 609.
Sisson, S. (2019).  New models for symbolic data analysis. 62nd ISI World Statistics Congress.
Duplantier, B., & Guttmann A. J. (2019).  New scaling laws for self-avoiding walks: bridges and worms. Journal of Statistical Mechanics: Theory and Experiment. 2019(10), 104010.
Zhang, M.., Shen M.., Liu F.., & Zhang H. (2019).  A new time and spatial fractional heat conduction model for Maxwell nanofluid in porous medium. Computers & Mathematics with Applications. 78(5), 1621-1636.
Vilas, M. P., Adams M. P., Ball M. C., Meynecke J-O., Santini N. S., Swales A., et al. (2019).  Night and day: Shrinking and swelling of stems of diverse mangrove species growing along environmental gradients. PLOS ONE. 14(9), e0221950.
Unknown, Filonik D., Feng T., Sun K., Nock R., Collins A., et al. (2019).  Non-Euclidean Embeddings for Graph Analytics and Visualisation. SIGGRAPH Asia 2019 PostersSIGGRAPH Asia 2019 Posters on - SA '19. 1 - 2.
Woodcock, S. (2019).  Not all equals are equal: Decoupling thinking processes and results in mathematical assessments. The Mathematics Education for the Future Project: 15th International Conference. 631-636.
Rasheed, A., Egodawatta P., Goonetilleke A., & McGree J. (2019).  A Novel Approach for Delineation of Homogeneous Rainfall Regions for Water Sensitive Urban Design—A Case Study in Southeast Queensland. Water. 11(3), 570.
Olson, R.., An S.-I.., Fan Y.., Chang W.., Evans J.. P., & Lee J.-Y.. (2019).  A novel method to test non-exclusive hypotheses applied to Arctic ice projections from dependent models. Nature Communications. 10(1), 3016.
Li, T.., Wang Y.., Liu F.., & Turner I. (2019).  Novel parameter estimation techniques for a multi-term fractional dynamical epidemic model of dengue fever. Numerical Algorithms. 82(4), 1467-1495.
Duncan, E. (2019).  Novel visualisations for a digital, interactive cancer atlas. OzViz Workshop 2019.
Li, W., Wang H-F., Li Z-Y., Wang T., & Zhao C-X. (2019).  Numerical investigation of drug transport from blood vessels to tumour tissue using a Tumour-Vasculature-on-a-Chip. Chemical Engineering Science. 208, 115155.
Chen, R.., Liu F.., & Anh V. (2019).  Numerical methods and analysis for a multi-term time-space variable-order fractional advection-diffusion equations and applications. Journal of Computational and Applied Mathematics. 352, 437-452.
Liu, F., & Turner I. (2019).  Numerical methods for time-space fractional partial differential equations. (Karniadakis, G. Em, Ed.).Handbook of Fractional Calculus with Applications. Volume 3: Numerical Methods, 209–248.
Liu, F., & Turner I. (2019).  Numerical methods for time-space fractional partial differential equations. (Karniadakis, G. Em, Ed.).Handbook of Fractional Calculus with Applications. Volume 3, Numerical Methods, 209-248.
Price, A. Elvey, & Guttmann A. J. (2019).  Numerical studies of Thompson’s group F and related groups. International Journal of Algebra and Computation. 29(02), 179 - 243.
Wu, P. Pao- Yen, Ruggeri F., & Mengersen KL. (2019).  Observational Uncertainty in Bayesian Networks and State Space Models. Bayes on the Beach 2019.
Talagala, P. Dilini, Hyndman R. J., & Smith-Miles K. (2019).  oddstream: Outlier Detection in Data Streams.
Talagala, P. Dilini (2019).  oddwater: Outlier Detection in Data from Water-Quality Sensors.
Nadarajah, K., Martin G. M., & Poskitt D. S. (2019).  Optimal bias-correction in the log-periodogram estimation of the fractional parameter: A jackknife approach. arXiv. arXiv:1903.07780.
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.
Beron, J., Benitez H., & Bovik A. (2019).  OPTIMAL FEATURE SELECTION FOR BLIND SUPER-RESOLUTION IMAGE QUALITY EVALUATION. ICASSP 2019.
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.
Forrester, P., & Trinh A. K. (2019).  Optimal soft edge scaling variables for the Gaussian and Laguerre even β ensembles. Nuclear Physics B. 938, 621 - 639.

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