Publications for 2019

Year of publication
2014 2015 2016 2017 2018 2019 Total
Number of publications included in citation count 9 208 135 126 134 120 742
Number of publications with at least one citation 18 198 126 115 110 45 603
Number of citations by year of citation 2014 6 3 0 0 0 0 9
2015 43 161 3 0 0 0 207
2016 48 566 100 2 0 0 716
2017 59 660 397 87 3 0 1206
2018 62 748 590 346 113 3 1862
2019 86 728 616 471 359 109 2369
Total citations by year of publication 304 2866 1706 906 475 112 6369

Book

Book - authored other

  1. Kroese, D. P., Botev, Z. I., Taimre, T., & Vaisman, R. (2019). Data Science and Machine Learning: Mathematical and Statistical Methods. Machine Learning & Pattern Recognition (1st ed.). Boca Raton: Chapman & Hall/CRC.

Book - edited

  1. Wood, D., de Gier, J., Praeger, C. E., & Tao, T. (Eds.) (2019). 2017 MATRIX Annals. MATRIX Book Series (Vol. 2). Cham: Springer International Publishing. Read online

Book Chapters

Book - chapter

  1. Asmussen, S., Goffard, P. - O., & Laub, P. J. (2019). Orthonormal Polynomial Expansions and Lognormal Sum Densities. In Barrieu, P. (Ed.), Risk and Stochastics (pp. 127-150). Europe: World Scientific. Read online
  2. Feher, G. Z., Garbali, A., De Gier, J, & Schoutens, K. (2019). A Curious Mapping Between Supersymmetric Quantum Chains. In Wood, D., de Gier, J., Praeger, C. E., & Tao, T. (Eds.), 2017 MATRIX Annals. MATRIX Book Series (Vol. 2, pp. 167-184). Springer, Cham. Read online
  3. Liu, F., & Turner, I.(2019). Numerical methods for time-space fractional partial differential equations. In Karniadakis, G. Em (Ed.), Handbook of Fractional Calculus with Applications (Vol. 3: Numerical Methods, pp. 209–248). Berlin, Boston: De Gruyter. Read online
  4. MacNamara, S., McLean, W., & Burrage, K. (2019). Wider contours and adaptive contours. In Wood, D., de Gier, J., Praeger, C. E., & Tao, T. (Eds.), 2017 MATRIX Annals. MATRIX Book Series (Vol. 2, pp. 79-98). Springer, Cham. Read online
  5. Mengersen, K., Duncan, E., Arbel, J., Alston-Knox, C., & White, N. (2019) Applications in Industry. In Fruhwirth-Schnatter, S., Celeux, G., & Robert, C. P. (Eds.), Handbook of Mixture Analysis (1st ed., Part III, Ch. 15). Boca Raton: Chapman & Hall/CRC Press. Read online
  6. Ye, N., Roosta-Khorasani, F., & Cui, T. (2019). Optimization Methods for Inverse Problems. In Wood, D., de Gier, J., Praeger, C. E., & Tao, T. (Eds) 2017 MATRIX Annals. MATRIX Book Series (Vol 2., pp. 121-140). Springer, Cham. Read online

Journal Articles

Article in scholarly refereed journal

  1. Akahori, J., Collevecchio, A., & Takei, M. (2019). Phase transitions for edge-reinforced random walks on the half-line. Electronic Communications In Probability, 24, 39. Read online
  2. Anderson, C., Hafen, R., Sofrygin, O., Ryan, L., & members of the HBGDki Community (2019). Comparing predictive abilities of longitudinal child growth models. Statistics In Medicine, 38(19), 3555 - 3570. Read online
  3. Asmussen, S., Laub, P., & Yang, H. (2019). Phase-Type Models in Life Insurance: Fitting and Valuation of Equity-Linked Benefits. Risks, 7(1), 17. Read online
  4. Aswi, A., Cramb, S., Moraga, P., & Mengersen, K. L. (2019). Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review. Epidemiology & Infection, 147, E33. Read online
  5. Austin, A. M., Douglass, M. J. J., Nguyen, G. T., Dalfsen, R., Le, H., Gorayski, P., Tee, H., Penniment, M., & Penfold, S. N. (2019). Cost-effectiveness of proton therapy in treating base of skull chordoma. Australasian Physical & Engineering Sciences In Medicine, 42(4), 1091 - 1098. Read online
  6. Ayyer, A., Finn, C., & Roy, D. (2019). The Phase Diagram for a Multispecies Left-Permeable Asymmetric Exclusion Process. Journal Of Statistical Physics, 174(3), 605-621. Read online
  7. Bagrow, J. P., Liu, X., & Mitchell, L. (2019). Information flow reveals prediction limits in online social activity. Nature Human Behaviour, 3(2), 122 - 128. Read online
  8. Barbour, A. D., & Roellin, A. (2019). Central limit theorems in the configuration model. Annals Of Applied Probability, 29(2), 1046-1069. Read online
  9. Barbour, A. D., & Xia, A. (2019). Multivariate approximation in total variation using local dependence. Electronic Journal Of Probability, 24, 27. Read online
  10. Barbour, A. D., Roellin, A., & Ross, N. (2019). Error bounds in local limit theorems using Stein's method. Bernoulli, 25(2), 1076-1104. Read online
  11. Bengston, S. A., Meissner, K. J., Menviel, L., Sisson, S. A., & Wilkin, J. (2019). Evaluating the extent of North Atlantic Deep Water and the mean Atlantic δ13C from statistical reconstructions. Paleoceanography And Paleoclimatology, 34(6), 1022-1036. Read online
  12. Benton, M. C., Lea, R. A., Macartney-Coxson, D., Sutherland, H. G., White, N., Kennedy, D., Mengersen, K., Haupt, L. M., & Griffiths, L. R. (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. Read online
  13. Beranger, B., Duong, T., Perkins-Kirkpatrick, S., & Sisson, S. A. (2019). Tail density estimation for exploratory data analysis using kernel methods. Journal Of Nonparametric Statistics, 31(1), 144-174. Read online
  14. Bilal, A., Rextin, A., Kakakhel, A., & Nasim, M. (2019). Analyzing Emergent Users’ Text Messages Data and Exploring Its Benefits. IEEE Access, 7, 2870 - 2879. Read online
  15. Black, A. J. (2019). Importance sampling for partially observed temporal epidemic models. Statistics And Computing, 29(4), 617-630. Read online
  16. Braunsteins, P., & Hautphenne, S. (2019). Extinction in lower Hessenberg branching processes with countably many types. The Annals Of Applied Probability, 29(5), 2782 - 2818. Read online
  17. Braunsteins, P., Decrouez, G., & Hautphenne, S. (2019). A pathwise approach to the extinction of branching processes with countably many types. Stochastic Processes And Their Applications, 129(3), 713-739. Read online
  18. Browning, A. P., Woodhouse, F. G., & Simpson, M. J. (2019). Reversible signal transmission in an active mechanical metamaterial. Proceedings Of The Royal Society A: Mathematical, Physical And Engineering Sciences, 475(2227), 20190146. Read online
  19. Budd, J. K., & Taylor, P. G. (2019). Bounds for the solution to the single-period inventory model with compound renewal process input: An application to setting credit card limits. European Journal Of Operational Research, 274(3), 1012 - 1018. Read online
  20. Béranger, B., Padoan, S. A., Xu, Y., & Sisson, S. A. (2019). Extremal properties of the multivariate extended skew-normal distribution, Part B. Statistics & Probability Letters, 147, 105 - 114. Read online
  21. Béranger, B., Padoan, S. A., Xu, Y., & Sisson, S. A. (2019). Extremal properties of the univariate extended skew-normal distribution, Part A. Statistics & Probability Letters, 147, 73 - 82. Read online
  22. Chakravorty, D., Banerjee, K., Mapder, T., & Saha, S. (2019). In silico modeling of phosphorylation dependent and independent c-Myc degradation. Bmc Bioinformatics, 20, 230. Read online
  23. Chew, J. S. C., Zhang, L., & Gan, H. S. (2019). Optimizing limited-stop services with vehicle assignment. Transportation Research Part E: Logistics And Transportation Review, 129, 228 - 246. Read online
  24. Colin, B., & Mengersen, K. (2019). Estimating Spatial and Temporal Trends in Environmental Indices Based on Satellite Data: A Two-Step Approach. Sensors, 19(2), 361. Read online
  25. Coller, J. K., Ramachandran, J., John, L., Tuke, J., Wigg, A., & Doogue, M. (2019). The impact of liver transplant recipient and donor genetic variability on tacrolimus exposure and transplant outcome. British Journal Of Clinical Pharmacology, 85(9), 2170-2175. Read online
  26. Cope, R. C., Ross, J. V., Wittmann, T. A., Watts, M. J., & Cassey, P. (2019). Predicting the Risk of Biological Invasions Using Environmental Similarity and Transport Network Connectedness. Risk Analysis, 39(1), 35-53. Read online
  27. Creutzig, T., Kanade, S., Liu, T., & Ridout, D. (2019). Cosets, characters and fusion for admissible-level. Nuclear Physics B, 938, 22 - 55. Read online
  28. Creutzig, T., Liu, T., Ridout, D., & Wood, S. (2019). Unitary and non-unitary N = 2 minimal models. Journal Of High Energy Physics, 2019(6), 024. Read online
  29. Dang, K. - D., Quiroz, M., Kohn, R., Tran, M. - N., & Villani, M. (2019). Hamiltonian Monte Carlo with Energy Conserving Subsampling. Journal Of Machine Learning Research, 20(100), 1-31.
  30. Dasgupta, P., Whop, L. J., Diaz, A., Cramb, S. M., Moore, S. P., Brotherton, J. M. L., Cunningham, J., Valery, P. C., Gertig, D., Garvey, G., Condon, J. R., O’Connell, D. L., Canfell, K., & Baade, P. D. (2019). Spatial variation in cervical cancer screening participation and outcomes among Indigenous and non-Indigenous Australians in Queensland. Geographical Research, 57(1), 111-122. Read online
  31. de Gier, J., Schadschneider, A., Schmidt, J., & Schütz, G. M. (2019). Kardar-Parisi-Zhang universality of the Nagel-Schreckenberg model. Physical Review E, 100(5), 052111. Read online
  32. de Micheaux, P. Lafaye, Liquet, B., & Sutton, M. (2019). PLS for Big Data: A unified parallel algorithm for regularised group PLS. Statistics Surveys, 13, 119-149. Read online
  33. Dendievel, S., Hautphenne, S., Latouche, G., & Taylor, P. G. (2019). The time-dependent expected reward and deviation matrix of a finite QBD process. Linear Algebra And Its Applications, 570, 61 - 92. Read online
  34. Deng, Y., Garoni, T. M., Grimm, J., Nasrawi, A., & Zhou, Z. (2019). The length of self-avoiding walks on the complete graph. Journal Of Statistical Mechanics: Theory And Experiment, 2019(10), 103206. Read online
  35. Ding, Z., Chen, B., Zhang, L., Jiang, R., Wu, Y., & Ding, J. (2019). Segment travel time route guidance strategy in advanced traveler information systems. Physica A: Statistical Mechanics And Its Applications, 534, 120432. Read online
  36. Duncan, E. W., Cramb, S. M., Aitken, J. F., Mengersen, K. L., & 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. Read online
  37. Feng, L. B., Liu, F., & Turner, I. (2019). Finite difference/finite element method for a novel 2D multi-term time-fractional mixed sub-diffusion and diffusion-wave equation on convex domains. Communications In Nonlinear Science And Numerical Simulation, 70, 354 - 371. Read online
  38. Feroz, F., Hobson, M. P., Cameron, E., & Pettitt, A. N. (2019). Importance Nested Sampling and the MultiNest Algorithm. The Open Journal Of Astrophysics, 2(1), 11120. Read online
  39. Fitzgerald, S. P., Beverborg, N. Grote, Beguin, Y., Artunc, F., Falhammar, H., & Bean, N. G. (2019). Population data provide evidence against the presence of a set point for hemoglobin levels or tissue oxygen delivery. Physiological Reports, 7(12), e14153. Read online
  40. Forrester, P. J. (2019). Meet Andréief, Bordeaux 1886, and Andreev, Kharkov 1882–1883. Random Matrices: Theory And Applications, 8(2), 1930001. Read online
  41. Forrester, P. J. (2019). Volumes for SLN(R), the Selberg Integral and Random Lattices. Foundations Of Computational Mathematics, 19(1), 55 - 82. Read online
  42. Forrester, P. J., & Ipsen, J. R.. (2019). A generalisation of the relation between zeros of the complex Kac polynomial and eigenvalues of truncated unitary matrices. Probability Theory And Related Fields, 175(3-4), 833 - 847. Read online
  43. Forrester, P. J., & Trinh, A. K. (2019). Finite‐size corrections at the hard edge for the Laguerre β ensemble. Studies In Applied Mathematics, 143(3), 315 - 336. Read online
  44. Forrester, P. J., & Trinh, A. K. (2019). Optimal soft edge scaling variables for the Gaussian and Laguerre even β ensembles. Nuclear Physics B, 938, 621 - 639. Read online
  45. Forrester, P. J., Ipsen, J. R., Liu, D. - Z., & Zhang, L. (2019). Orthogonal and symplectic Harish-Chandra integrals and matrix product ensembles. Random Matrices: Theory And Applications, 8(4), 1950015. Read online
  46. Forrester, P. J., Perk, J. H. H., Trinh, A. K., & Witte, N. S. (2019). Leading corrections to the scaling function on the diagonal for the two-dimensional Ising model. Journal Of Statistical Mechanics: Theory And Experiment, 2019(2), 023106. Read online
  47. Foumani, M., & Smith-Miles, K. (2019). The impact of various carbon reduction policies on green flowshop scheduling. Applied Energy, 249, 300 - 315. Read online
  48. Froese, J. G., Pearse, A. R., Hamilton, G., & Silvestro, D. (2019). Rapid spatial risk modelling for management of early weed invasions: Balancing ecological complexity and operational needs. Methods In Ecology And Evolution, 10(12), 2105-2117. Read online
  49. Glonek, M., Tuke, J., Mitchell, L., & Bean, N. (2019). Semi-supervised graph labelling reveals increasing partisanship in the United States Congress. Applied Network Science, 4(1), 62. Read online
  50. Gray, C., Mitchell, L., Roughan, M., & Gleeson, J. (2019). Generating connected random graphs. Journal Of Complex Networks, 7(6), 896 - 912. Read online
  51. Gunawan, D., Tran, M. - N., Suzuki, K., Dick, J., & Kohn, R. (2019). Computationally efficient Bayesian estimation of high-dimensional Archimedean copulas with discrete and mixed margins. Statistics And Computing, 29(5), 933-946. Read online
  52. Guo, L., Zeng, F., Turner, I., Burrage, K., & Karniadakis, G. E. (2019). Efficient Multistep Methods for Tempered Fractional Calculus: Algorithms and Simulations. Siam Journal On Scientific Computing, 41(4), A2510 - A2535. Read online
  53. Haller-Bull, V., Bode, M., & Hewitt, J. (2019). Superadditive and subadditive dynamics are not inherent to the types of interacting threat. PLOS One, 14(8), e0211444. Read online
  54. Harris, D., Martin, G. M., Perera, I., & Poskitt, D. S. (2019). Construction and Visualization of Confidence Sets for Frequentist Distributional Forecasts. Journal Of Computational And Graphical Statistics, 28(1), 92-104. Read online
  55. Herger, N., Abramowitz, G., Sherwood, S., Knutti, R., Angelil, O., & Sisson, S. A. (2019). Ensemble optimisation, multiple constraints and overconfidence: a case study with future Australian precipitation change. Climate Dynamics, 53(3-4), 1581-1596. Read online
  56. Holloway, J., Helmstedt, K. J., Mengersen, K., & Schmidt, M. (2019). A Decision Tree Approach for Spatially Interpolating Missing Land Cover Data and Classifying Satellite Images. Remote Sensing, 11(15), 1796. Read online
  57. Iseriles, A., & MacNamara, S. (2019). Applications of Magnus expansions and pseudospectra to Markov processes. European Journal Of Applied Mathematics, 30(2), 400-425. Read online
  58. Jiang, Y., Wang, Y. - G., Fu, L., & Wang, X. (2019). Robust Estimation Using Modified Huber’s Functions With New Tails. Technometrics, 61(1), 111-122. Read online
  59. Kawasetsu, K., & Ridout, D. (2019). Relaxed Highest-Weight Modules I: Rank 1 Cases. Communications In Mathematical Physics, 368(2), 627 - 663. Read online
  60. Keith, J. M., Spring, D., & Kompas, T. (2019). Delimiting a species’ geographic range using posterior sampling and computational geometry. Scientific Reports, 9(1), 8938. Read online
  61. Kieburg, M., Forrester, P. J., & Ipsen, J. R. (2019). Multiplicative convolution of real asymmetric and real anti-symmetric matrices. Advances In Pure And Applied Mathematics, 10(4), 467 - 492. Read online
  62. Komori, Y., Eremin, A., & Burrage, K. (2019). S-ROCK methods for stochastic delay differential equations with one fixed delay. Journal Of Computational And Applied Mathematics, 353, 345-354 /Read online
  63. Kuhn, J., Mandjes, M., & Taimre, T. (2019). Practical Aspects of False Alarm Control for Change Point Detection: Beyond Average Run Length. Methodology And Computing In Applied Probability, 21(1), 25-42. Read online
  64. 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. Read online
  65. Leigh, C., Alsibai, O., Hyndman, R. J., Kandanaarachchi, S., King, O. C., McGree, J. M., Neelamraju, C., Strauss, J., Talagala, P. D., Turner, R. D. R., Mengersen, K., & Peterson, E. E. (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. Read online
  66. Leigh, C., Aspin, T. W. H., Matthews, T. J., Rolls, R. J., & Ledger, M. E. (2019). Drought alters the functional stability of stream invertebrate communities through time. Journal Of Biogeography, 46(9), 1988 - 2000. Read online
  67. Leigh, C., Boersma, K. S., Galatowitsch, M. L., Milner, V. S., Stubbington, R., & Gibbs, L. (2019). Are all rivers equal? The role of education in attitudes towards temporary and perennial rivers. People And Nature, 1(2), 181-190. Read online
  68. Leigh, C., Kandanaarachchi, S., McGree, J. M., Hyndman, R. J., Alsibai, O., Mengersen, K., & Peterson, E. E. (2019). Predicting sediment and nutrient concentrations from high-frequency water-quality data. PLOS One, 14(8), e0215503. Read online
  69. Li, C., Xie, H. - B., Fan, X., Da Xu, R. Yi, Van Huffel, S., Sisson, S. A., & Mengersen, K. (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. Read online
  70. 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. Read online
  71. Liquet, B., & Riou, J. (2019). CPMCGLM: an R package for p-value adjustment when looking for an optimal transformation of a single explanatory variable in generalized linear models. Bmc Medical Research Methodology, 19, 79. Read online
  72. M. MacNeil, A., Mellin, C., Matthews, S., Wolff, N. H., McClanahan, T. R., Devlin, M., Drovandi, C., Mengersen, K., & Graham, N. A. J. (2019). Water quality mediates resilience on the Great Barrier Reef. Nature Ecology & Evolution, 3(4), 620 - 627. Read online
  73. Mapder, T., Clifford, S., Aaskov, J., Burrage, K., & Papin, J. A. (2019). A population of bang-bang switches of defective interfering particles makes within-host dynamics of dengue virus controllable. PLOS Computational Biology, 15(11), e1006668. Read online
  74. 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. Read online
  75. Ockelford, A., Woodcock, S., & Haynes, H. (2019). The impact of inter‐flood duration on non‐cohesive sediment bed stability. Earth Surface Processes And Landforms, 44(14), 2861 - 2871. Read online
  76. 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. Read online
  77. Padgham, M., Boeing, G., Cooley, D., Tierney, N., Sumner, M., Phan, T. G., & Beare, R. (2019). An Introduction to Software Tools, Data, and Services for Geospatial Analysis of Stroke Services. Frontiers In Neurology, 10, 743. Read online
  78. 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. Read online
  79. Pařil, P., Leigh, C., Polášek, M., Sarremejane, R., Řezníčková, P., Dostálová, A., & Stubbington, R. (2019). Short-term streambed drying events alter amphipod population structure in a central European stream. Fundamental And Applied Limnology / Archiv Für Hydrobiologie, 193(1), 51-64 .Read online
  80. Peterson, E. E., Hanks, E. M., Hooten, M. B., Hoef, J. M. Ver, & Fortin, M. - J. (2019). Spatially structured statistical network models for landscape genetics. Ecological Monographs, 89(2), e01355. Read online
  81. 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. Read online
  82. 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. Read online
  83. Quiroz, M., Kohn, R., Villani, M., & Tran, M. N. (2019). Speeding Up MCMC by Efficient Data Subsampling. Journal Of The American Statistical Association, 114(526), 831-843. Read online
  84. Rodrigues, T., Dortet-Bernadet, J. - L., & Fan, Y. (2019). Pyramid Quantile Regression. Journal Of Computational And Graphical Statistics, 28(3), 732 - 746. Read online
  85. Rodrigues, T., Dortet-Bernadet, J. - L., & Fan, Y. (2019). Simultaneous fitting of Bayesian penalised quantile splines. Computational Statistics & Data Analysis, 134, 93 - 109. Read online
  86. Roosta-Khorasani, F., & Mahoney, M. W. (2019). Sub-sampled Newton methods. Mathematical Programming, 174(1-2), 293 - 326. Read online
  87. Roughan, M. (2019). Practically surreal: Surreal arithmetic in Julia. Softwarex, 9, 293 - 298. Read online
  88. Sallustio, B. C., Noll, B. D., Coller, J. K., Tuke, J., Russ, G., & Somogyi, A. A. (2019). Relationship between allograft cyclosporin concentrations and P-glycoprotein expression in the 1st month following renal transplantation. British Journal Of Clinical Pharmacology, 85(5), 1015–1020. Read online
  89. Santos-Fernández, E., Wu, P., & Mengersen, K. L. (2019). Bayesian statistics meets sports: a comprehensive review. Journal Of Quantitative Analysis In Sports, 15(4), 289 - 312. Read online
  90. 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. Read online
  91. Shao, Q. - M., & Zhang, Z. (2019). Berry-Esseen bound of normal and Nonnormal approximation for unbounded exchangeable pairs. Annals Of Probability, 47(1), 61-108. Read online
  92. 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. Read online
  93. Shi, Y., Liu, F., Zhao, Y., Wang, F., & Turner, I. (2019). An unstructured mesh finite element method for solving the multi-term time fractional and Riesz space distributed-order wave equation on an irregular convex domain. Applied Mathematical Modelling, 73, 615-636. Read online
  94. Shumilova, O., Zak, D., Datry, T., von Schiller, D., Corti, R., Foulquier, A., Obrador, B., Tockner, K., Allan, D. C., Altermatt, F., Arce, M. I., Arnon, S., Banas, D., Banegas-Medina, A., Beller, E., Blanchette, M. L., Blanco-Libreros, J. F., Blessing, J., Gonçalves Boëchat, I., Boersma, K., Bogan, M. T., Bonada, N., Bond, N. R., Brintrup, K., Bruder, A., Burrows, R., Cancellario, T., Carlson, S. M., Cauvy-Fraunié, S., Cid, N., Danger, M., de Freitas Terra, B., De Girolamo, A. M., del Campo, R., Dyer, F., Elosegi, A., Faye, E., Febria, C., Figueroa, R., Four, B., Gessner, M. O., Gnohossou, P., Gómez Cerezo, R., Gomez-Gener, L., Graça, M. A. S, Guareschi, S., Gücker, B., Hwan, J. L., Kubheka, S., Langhans, S. D., Leigh, C., Little, C. J., Lorenz, S., Marshall, J., McIntosh, A., Mendoza-Lera, C., Meyer, E. I., Miliša, M., Mlambo, M. C., Moleón, M., Negus, P., Niyogi, D., Paptheodoulou, A., Pardo, I., Paril, P., Pešić, V., Rodriguez-Lozano, P., Rolls, R. J., Sanchez-Montoya, M. M., Savić, A., Steward, A., Stubbington, R., Taleb, A., Vaner Vorste, R., Waltham, N., Zoppini, A., & Zarfl, C.  (2019). Simulating rewetting events in intermittent rivers and ephemeral streams: A global analysis of leached nutrients and organic matter. Global Change Biology, 25(5), 1591 - 1611. Read online
  95. Sonenberg, N., & Taylor, P. G. (2019). Networks of interacting stochastic fluid models with infinite and finite buffers. Queueing Systems, 92(3-4), 293 - 322. Read online
  96. South, L. F., Pettitt, A. N., & Drovandi, C. C. (2019). Sequential Monte Carlo Samplers with Independent Markov Chain Monte Carlo Proposals. Bayesian Analysis, 14(3), 753 - 776. Read online
  97. Suhr, E. L., O'Dowd, D. J., Suarez, A. V., Cassey, P., Wittmann, T. A., Ross, J. V., & Cope, R. C. (2019). Ant interceptions reveal roles of transport and commodity in identifying biosecurity risk pathways into Australia. Neobiota, 53, 1 - 24. Read online
  98. Sutton, M., Mengersen, K. L., & Liquet, B. (2019). [HDDA] sparse subspace constrained partial least squares. Journal Of Statistical Computation And Simulation, 89(6), 1005-1019. Read online
  99. Talagala, P. Dilini, Hyndman, R. J., Leigh, C., Mengersen, K., & 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. Read online
  100. Tang, M., Gandhi, N. S., Burrage, K., & Gu, Y. T. (2019). Interaction of gold nanosurfaces/nanoparticles with collagen-like peptides. Physical Chemistry Chemical Physics , 21(7), 3701-3711. Read online
  101. Thomas, A., White, N. M., Toms, L. - M. Leontjew, Mengersen, K., & Garrett, T. J. (2019). Application of ensemble methods to analyse the decline of organochlorine pesticides in relation to the interactions between age, gender and time. PLOS One, 14(11), e0223956. Read online
  102. Tierney, N. J., Mira, A., H. Reinhold, J., Arbia, G., Clifford, S., Auricchio, A., Moccetti, T., Peluso, S. & Mengersen, K. L. (2019). Evaluating health facility access using Bayesian spatial models and location analysis methods. PLOS One, 14(8), e0218310. Read online
  103. Ullah, I., & Mengersen, K. (2019). Bayesian mixture models and their Big Data implementations with application to invasive species presence-only data. Journal Of Big Data, 6(1), 29. Read online
  104. Ullah, I., Paul, S., Hong, Z., & Wang, Y. - G. (2019). Significance tests for analyzing gene expression data with small sample sizes. Bioinformatics, 35(20), 3996 - 4003. Read online
  105. Van Looy, K., Tonkin, J. D., Floury, M., Leigh, C., Soininen, J., Larsen, S., Heino, J., LeRoy Poff, N., Delong, M., Jähnig, S. C., Datry, T., Bonada, N., Rosebury, J., Jamoneau, A., Ormerod, S. J., Collier, K. J., & Wolter, C. (2019). The three Rs of river ecosystem resilience: Resources, recruitment, and refugia. River Research And Applications, 35(2), 107 - 120Read online
  106. Varney, J., Bean, N. G., & Mackay, M. (2019). The self-regulating nature of occupancy in ICUs: stochastic homoeostasis. Health Care Management Science, 22(4), 615-634. Read online
  107. Vo, B. N., Drovandi, C. C., & Pettitt, A. N. (2019). Bayesian Parametric Bootstrap for Models with Intractable Likelihoods. Bayesian Analysis, 14(1), 211 - 234. Read online
  108. von Schiller, D., Datry, T., Corti, R., Foulquier, A., Tockner, K., Marcé, R., García-Bacquero, G., Odriozola, I., Obrador, B., Elosegi, A., Mendoza‐Lera, C., Gessner, M. O., Stubbington, R., Albariño, R., Allen, D. C., Altermatt, F., Arce, M. I., Arnon, S., Banas, D., Banegas‐Medina, A., Beller, E., Blanchette, M. L., Blanco‐Libreros, J. F., Blessing, J., Boëchat, I. G., Boersma, K. S., Bogan, M. T., Bonada, N., Bond, N. R., Brintrup, K., Bruder, A., Burrows, R. M., Cancellario, T., Carlson, S. M., Cauvy‐Fraunié, S., Cid, N., Danger, M., de Freitas Terra, B., Dehedin, A., De Girolamo, A. M., del Campo, R., Díaz‐Villanueva, V., Duerdoth, C. P., Dyer, F., Faye, E., Febria, C., Figueroa, R., Four, B., Gafny, S., Gómez, R., Gómez‐Gener, L., Graça, M. A. S., Guareschi, S., Gücker, B., Hoppeler, F., Hwan, J. L., Kubheka, S., Laini, A., Langhans, S. D., Leigh, C., Little, C. J., Lorenz, S., Marshall, J., Martín, E. J., McIntosh, A., Meyer, E. I., Miliša, M., Mlambo, M. C., Moleón, M., Morais, M., Negus, P., Niyogi, D., Papatheodoulou, A., Pardo, I., Pařil, P., Pešić, V., Piscart, C., Polášek, M., Rodríguez‐Lozano, P., Rolls, R. J., Sánchez‐Montoya, M. M., Savić, A., Shumilova, O., Steward, A., Taleb, A., Uzan, A., Vander Vorste, R., Waltham, N., Woelfle‐Erskine, C., Zak, D., Zarfl, C., & Zoppini, A. (2019). Sediment Respiration Pulses in Intermittent Rivers and Ephemeral Streams. Global Biogeochemical Cycles, 33(10), 1251-1263. Read online
  109. Walker, J. N., Black, A. J., & Ross, J. V. (2019). Bayesian model discrimination for partially-observed epidemic models. Mathematical Biosciences, 317, 108266. Read online
  110. Wang, Z. - L., & Li, S. - H. (2019). BKP hierarchy and Pfaffian point process. Nuclear Physics B, 939, 447 - 464. Read online
  111. Weerasinghe, H. N., Burrage, P. M., Burrage, K., & Nicolau, D. V. (2019). Mathematical Models of Cancer Cell Plasticity. Journal Of Oncology, 2019, 2403483. Read online
  112. 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. Read online
  113. Wijerathne, W. D. C. C., Rathnayaka, C. M., Karunasena, H. C. P., Senadeera, W., Sauret, E., Turner, I. W., & Gu, Y. T. (2019). A coarse-grained multiscale model to simulate morphological changes of food-plant tissues undergoing drying. Soft Matter, 15(5), 901 - 916. Read online
  114. 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. Read online
  115. Wu, P. Pao- Yen, Sterkenburg, N., Everett, K., Chapman, D. W., White, N., & Mengersen, K. (2019). Predicting fatigue using countermovement jump force-time signatures: PCA can distinguish neuromuscular versus metabolic fatigue. PLOS One, 14(7), e0219295. Read online
  116. Wu, P. Pao‐Yen, Mengersen, K., M. Caley, J., McMahon, K., Rasheed, M. A., Kendrick, G. A., & Poisot, T. (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. Read online
  117. Zeng, F., Turner, I., Burrage, K., & Wright, S. J. (2019). A discrete least squares collocation method for two-dimensional nonlinear time-dependent partial differential equations. Journal Of Computational Physics, 394, 177 - 199. Read online
  118. Zhang, X., Wen, F., & de Gier, J. (2019). T-Q relations for the integrable two-species asymmetric simple exclusion process with open boundaries. Journal Of Statistical Mechanics: Theory And Experiment, 2019(1), 014001. Read online
  119. Zhang, Y., Bambrick, H., Mengersen, K., Tong, S., Feng, L., Zhang, L., Liu, G., Xu, A., & Hu, W. (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. Read online
  120. Zhou, X., Bueno-Orovio, A., Schilling, R. J., Kirkby, C., Denning, C., Rajamohan, D., Burrage, K., Tinker, C., Rodriguez. B., & Harmer, S. C. (2019). Investigating the complex arrhythmic phenotype caused by the gain-of-function mutation KCNQ1-G229D. Frontiers In Physiology, 10, 259. Read online

Letter or note

  1. An, Z., L. F. South, Nott, D. J., & Drovandi, C. C. (2019). Accelerating Bayesian Synthetic Likelihood with the Graphical Lasso. Journal Of Computational And Graphical Statistics, 28(2), 471-475. Read online
  2. Cespedes, M. I. (2019). Detection of Longitudinal Brain Atrophy Patterns Consistent with Progression Towards Alzheimer’s Disease. Bulletin Of The Australian Mathematical Society, 99(1), 174 - 176. Read online
  3. Forrester, P. J., & Kumar, S. (2019). Recursion scheme for the largest β -Wishart–Laguerre eigenvalue and Landauer conductance in quantum transport. Journal Of Physics A: Mathematical And Theoretical, 52(42), 42LT02. Read online
  4. Forrester, P. J., & Trinh, A. K. (2019). Comment on “Finite size effects in the averaged eigenvalue density of Wigner random-sign real symmetric matrices”. Physical Review E, 99(3), 036101. Read online
  5. Ryan, L. (2019). Four papers on child growth modelling. Statistics In Medicine, 38, 3505-3506. Read online

Non-refereed article

  1. Bardsley, J., Cui, T., Marzouk, Y., & Wang, Z. (2019). Scalable optimization-based sampling on function space. Arxiv, arXiv:1903.00870v2. Read online
  2. Bean, N., Lewis, A., & Nguyen, G. (2019). Estimation of Markovian-regime-switching models with independent regimes. Arxiv, arXiv:1906.07957v1. Read online
  3. Bean, N., Nguyen, G. T., O'Reilly, M. M., & Sunkara, V. (2019). A discontinuous Galerkin method for approximating the stationary distribution of stochastic fluid-fluid processes. Arxiv, arXiv:1901.10635v1. Read online
  4. Bean, N. G., O'Reilly, M. M., & Palmowski, Z. (2019). Yaglom limit for Stochastic Fluid Models. Arxiv, arXiv:1908.10827v1. Read online
  5. Chin, V., Gunawan, D., Fiebig, D. G., Kohn, R., & Sisson, S. A. (2019). Efficient data augmentation for multivariate probit models with panel data: An application to general practitioner decision-making about contraceptives. Arxiv, arXiv:1806.07274v2. Read online
  6. Crotty, S. M., Minh, B. Quang, Bean, N. G., Holland, B. R., Tuke, J., Jermiin, L. S., & von Haeseler, A. (2019). GHOST: recovering historical signal from heterotachously-evolved sequence alignments. Biorxiv, 174789. Read online
  7. Cui, T., Detommaso, G., & Scheichl, R. (2019). Multilevel Dimension-Independent Likelihood-Informed MCMC for Large-Scale Inverse problems. Arxiv, arXiv:1910.12431v1. Read online
  8. De Sterck, H., Falgout, R., Friedhoff, S., Krzysik, O., & MacLachlan, S. (2019). Optimizing MGRIT and Parareal coarse-grid operators for linear advection. Arxiv, arXiv:1910.03726v1. Read online
  9. Eshragh, A., Roosta, F., Nazari, A., & Mahoney, M. W. (2019). LSAR: Efficient Leverage Score Sampling Algorithm for the Analysis of Big Time Series Data. Arxiv, arXiv:1911.12321v2. Read online
  10. Fang, S., Grimm, J., Zhou, Z., & Deng, Y. (2019). Complete graph and Gaussian fixed point asymptotics in the five-dimensional Fortuin-Kasteleyn Ising model with periodic boundaries. Arxiv, arXiv:1909.04328v2.  Read online 
  11. Frazier, D. T., & Drovandi, C. (2019). Robust Approximate Bayesian Inference with Synthetic Likelihood. Arxiv, arXiv:1904.04551v2. Read online
  12. Frazier, D. T., Nott, D. J., Drovandi, C., & Kohn, R. (2019). Bayesian inference using synthetic likelihood: asymptotics and adjustments. Arxiv, arXiv:1902.04827v2. Read online
  13. Garbali, A., & Wheeler, M.. (2019). Modified Macdonald polynomials and integrability. Arxiv, arXiv:1810.12905v2. Read online
  14. Glenny, V., Tuke, J., Bean, N., & Mitchell, L. (2019). A framework for streamlined statistical prediction using topic models. Arxiv, arXiv:1904.06941v1. Read online
  15. Glenny, V., Tuke, S. Jonathan, Bean, N., & Mitchell, L. (2019). In the Humanities and Social Sciences, there is increasing interest in approaches to information extraction, prediction, intelligent linkage, and dimension reduction applicable to large text corpora. With approaches in these fields being grounded in tradi. Arxiv, arXiv:1904.06941v1. Read online
  16. Glonek, M., Tuke, J., Mitchell, L., & Bean, N. (2019). Semi-supervised graph labelling reveals increasing partisanship in the United States Congress. Arxiv, arXiv:1904.01153v2. Read online
  17. Hamza, A., Ranathunga, D., Gharakheili, H. Habibi, Benson, T. A., Roughan, M., & Sivaraman, V. (2019). Verifying and Monitoring IoTs Network Behavior using MUD Profiles. Arxiv, arXiv:1902.02484v1. Read online
  18. Hodgkinson, L., Salomone, R., & Roosta, F. (2019). Implicit Langevin Algorithms for Sampling From Log-concave Densities. Arxiv, arXiv:1903.12322v1. Read online
  19. Kang, Y., Hyndman, R. J., & Li, F. (2019). GRATIS: GeneRAting TIme Series with diverse and controllable characteristics. Arxiv, arXiv:1903.02787v1. Read online
  20. Kennedy, D. W., White, N. M., Benton, M. C., Lea, R. A., & Mengersen, K. (2019). Cell-type specific analysis of heterogeneous methylation signal using a Bayesian model-based approach. Biorxiv, 682070. Read online
  21. Levin, K., Roosta, F., Tang, M., Mahoney, M. W., & Priebe, C. E. (2019). Limit theorems for out-of-sample extensions of the adjacency and Laplacian spectral embeddings. Arxiv, arXiv:1910.00423v1. Read online
  22. Liu, Y., & Roosta, F. (2019). Stability Analysis of Newton-MR Under Hessian Perturbations. Arxiv, arXiv:1909.06224v1 .Read online
  23. Loaiza-Maya, R., Martin, G. M., & Frazier, D. T. (2019). Focused Bayesian Prediction. Arxiv, arXiv:1912.12571v1. Read online
  24. Mandal, S., Bhattacharya, S., Roy, C., Rameez, M. Jameela, Sarkar, J., Fernandes, S., Mapder, T., Peketi, A., Mazumdar, A., & Ghosh, W. (2019). Cryptic role of tetrathionate in the sulfur cycle: A study from Arabian Sea oxygen minimum zone sediments. Biorxiv, 686469. Read online
  25. Mapder, T., Aaskov, J., & Burrage, K. (2019). Administration of defective virus via bang-bang optimal control inhibits dengue transmission. Biorxiv. Read online
  26. Mayer, I., Josse, J., Tierney, N., & Vialaneix, N. (2019). R-miss-tastic: a unified platform for missing values methods and workflows. Arxiv, arXiv:1908.04822v1. Read online
  27. McArthur, L., & Humphries, M. A. (2019). Multi-model mimicry for model selection according to generalisedgoodness-of-fit criteria. Arxiv, arXiv:1911.09779v2. Read online
  28. Moka, S. B., & Kroese, D. P. (2019). Perfect Sampling for Gibbs Point Processes Using Partial Rejection Sampling. Arxiv, arXiv:1901.05624v1. Read online
  29. Mondal, N., Roy, C., Peketi, A., Alam, M., Mapder, T., Mandal, S., Fernandes, S., Bhattacharya, S., Rameez, M. J., Haldar, P. K., Volvoikar, S. P., Nandi, N., Bhattacharya, T., Mazumdar, A., Chakraborty, R., & Ghosh, W. (2019). Inorganic salts and compatible solutes help mesophilic bacteria inhabit the high temperature waters of a Trans-Himalayan sulfur-borax spring. Biorxiv, 678680. Read online
  30. Priddle, J. W., Sisson, S. A., & Drovandi, C. C. (2019). Efficient Bayesian synthetic likelihood with whitening transformations. Arxiv, arXiv:1909.04857v1. Read online
  31. Roughan, M., Mitchell, L., & South, T. (2019). How the Avengers assemble: Ecological modelling of effective cast sizes for movies. Arxiv, arXiv:1906.08403v1. Read online
  32. Salomone, R., Quiroz, M., Kohn, R., Villani, M., & Tran, M. - N. (2019). Spectral Subsampling MCMC for Stationary Time Series. Arxiv, arXiv:1910.13627v1.  Read online
  33. Sharp, J. A., Browning, A. P., Mapder, T., Baker, C. M., Burrage, K., & Simpson, M. J. (2019). Designing combination therapies using multiple optimal controls. Biorxiv. Read online
  34. Tafakori, L., Pourkhanali, A., & Rastelli, R. (2019). Measuring systemic risk and contagion in the European financial network. Arxiv, arXiv:1911.11488v1. Read online
  35. Talagala, P. Dilini, Hyndman, R. J., & Smith-Miles, K. (2019). Anomaly Detection in High Dimensional Data. Arxiv, arXiv:1908.04000v1. Read online
  36. Teo, M., Bean, N., & Ross, J. (2019). Optimised prophylactic vaccination in metapopulations. Biorxiv, 559732. Read online
  37. Tsuchida, R., Roosta, F., & Gallagher, M. (2019). Richer priors for infinitely wide multi-layer perceptrons. Arxiv, arXiv:1911.12927v1. Read online
  38. Warne, D. J., Sisson, S. A., & Drovandi, C. (2019). Acceleration of expensive computations in Bayesian statistics using vector operations. Arxiv, arXiv:1902.09046v2. Read online

Invited talks, refereed proceedings and other conference outputs

Full written conference paper (refereed - must be peer reviewed and presented)

  1. Aswi, A., Cramb, S., Hu, W., White, G., & Mengersen, K. (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). Makassar, Indonesia. Trans Tech Publications, Materials Science Forum, 967, 15-21. Read online
  2. Crane, R., & Roosta, F. (2019). DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada. Read online
  3. Fan, X., Li, B., Sisson, S., Chaudhuri, K., & Sugiyama, M. (2019). Binary Space Partitioning Forest. The 22nd International Conference on Artificial Intelligence and Statistics (AISTATS) 2019. Okinawa, Japan. Proceedings of Machine Learning Research, 89, 3022-3031. Read online
  4. Fan, X., Li, B., Sisson, S. A., Li, C., & Chen, L. (2019). Scalable deep generative relational model with high-order node dependence. 33rd Conference on Neural Information Processing Systems (NeurIPS 2019). Vancouver, Canada. Read online
  5. Fu, J., Nazarathy, Y., Moka, S. B., & Taylor, P. (2019). Towards Q-learning the Whittle Index for Restless Bandits. 2019 Australian & New Zealand Control Conference (ANZCC). Auckland, New Zealand. IEEE, 249-254. Read online
  6. Glenny, V., Tuke, J., Bean, N., & Mitchell, L. (2019). A framework for streamlined statistical prediction using topic models. 3rd Joint SIGHUM Workshop on Computational Linguistics for Cultural Heritage, Social Sciences, Humanities and Literature. Minneapolis, USA. Association for Computational Linguistics, 61-70. Read online
  7. Glonek, M., Tuke, J., Mitchell, L., & Bean, N. (2019). GLaSS: Semi-supervised Graph Labelling with Markov Random Walks to Absorption. In Aiello, L., Cherifi, C., Cherifi, H., Lambiotte, R., Lió, P., & Rocha, L. (Eds.) Studies in Computational Intelligence, 812, 304-315. Read online
  8. Kylasa, S., Roosta, F., Mahoney, M. W., Grama, A., Berger-Wolf, T., & Chawla, N. (2019). GPU Accelerated Sub-Sampled Newton's Method for Convex Classification Problems. SIAM International Conference on Data Mining. Alberta, Canada. SIAM, 702-710. Read online
  9. Moka, S. Babu, Kroese, D. P., & Juneja, S. (2019). Unbiased Estimation of The Reciprocal Mean For Non-Negative Random Variables. 2019 Winter Simulation Conference (WSC). National Harbor, MD, USA. IEEE, 404-415. Read online
  10. Nguyen, A., South, T., Bean, N. G., Tuke, J., & Mitchell, L. (2019). Podlab at SemEval-2019 Task 3: The Importance of Being Shallow. 13th International Workshop on Semantic Evaluation. Minnesota, USA. Association for Computational Linguistics, 292-296. Read online
  11. Rasheed, O., Rextin, A., & Nasim, M. (2019). Adult or Child: Recognizing through Touch Gestures on Smartphones. 2019 Digital Image Computing: Techniques and Applications (DICTA). Perth, Australia. IEEE, 8945816. Read online
  12. Ridall, G., George, M., & Pettitt, A. N. (2019). Fast sequential Bayesian analysis of football scores illustrating the evolution of the styles and strengths of each of the British premier league football sides over two decades. 34th International Workshop on Statistical Modeling. Guimaraes, Portugal. EPIUnit, ICBADS, University of Porto, 2, 267-272. Read online
  13. Roughan, M., Tuke, J., & Parsonage, E. (2019). Estimating the parameters of the Waxman random graph. 16th Workshop on Algorithms and Models for the Web Graph (WAW 2019). Brisbane, Australia. Lecture Notes in Computer Science, 11631, 71-86. Read online
  14. Samuelson, A., O'Reilly, M. M., & Bean, N. G. (2019). Construction of algorithms for discrete-time quasi-birth-and-death processes through physical interpretation. The 10th International Conference on Matrix-Analytic Methods in Stochastic Models. Hobart, Australia. Discipline of Mathematics, University of Tasmania, 53-57.  Read online
  15. Sarker, C., Mejias, L., Maire, F., & Woodley, A. (2019). Evaluation of the Impact of Image Spatial Resolution in Designing a Context-Based Fully Convolution Neural Networks for Flood Mapping. 2019 Digital Image Computing: Techniques and Applications (DICTA). Perth, Australia. IEEE, 8945888. Read online
  16. Schadschneider, A., Schmidt, J., De Gier, J., & Schütz, G. M. (2019). Dynamical Universality Class of the Nagel–Schreckenberg and Related Models. In S. H. Hamdar (Ed.) Traffic and Granular Flow '17. Washington, USA. Springer, 53-60. Read online
  17. Tsuchida, R., Roosta, F., Gallagher, M., Kraus, S., & Eiter, T. (2019). Exchangeability and Kernel Invariance in Trained MLPs. Twenty-Eighth International Joint Conference on Artificial Intelligence. Macao, China. International Joint Conferences on Artificial Intelligence Organization, 3592-3598. Read online
  18. Wang, E., Kurniawati, H., & Kroese, D. P. (2019). Inventory control with partially observable states. 23rd International Congress on Modelling and Simulation (MODSIM2019). Canberra, Australia. Modelling and Simulation Society of Australia and New Zealand, 200-206. Read online
  19. Woodley, A., McLaughlin, C., Hutson, H., Geva, S., Chappell, T., Kelly, W., Perrin, D., Boles, W., & De Vine, L. (2019). High Resolution Change Detection Using Planet Mosaic. 2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama, Japan. IEEE, 6578-6581. Read online
  20. Xu, M., Quiroz, M., Kohn, R., Sisson, S. A., Chaudhuri, K., & Sugiyama, M. (2019). Variance reduction properties of the reparameterisation trick. The 22nd International Conference on Artificial Intelligence and Statistics. Proceedings of Machine Learning Research, 89, 2711-2720. Read online

Full written paper (non refereed - not subject to peer review but were formally assessed by an editorial board)

  1. Martin, G. (2019). Focused Bayesian Prediction. Statistical Methods in Data Science. Creswick, Australia: MATRIX.

Edited volume of conference proceedings (edited volumes in which one or more staff members have editorial responsibility)

  1. Avrachenkov, K., Prałat, P., & Ye, N. (Eds.) (2019). Algorithms and Models for the Web Graph. 16th International Workshop, WAW 2019. Brisbane, Australia: Springer International Publishing. Read online
  2. Hautphenne, S., O'Reilly, M., & Poloni, F. (Eds.) (2019). Matrix-Analytic Methods in Stochastic Models. The 10th International Conference on Matrix-Analytic Methods in Stochastic Models. Hobart, Australia: Discipline of Mathematics, University of Tasmania. Read online

Extract of paper (abstracts, extracts and synopses of conference papers that are subsequently published)

  1. Abera, A. K., O'Reilly, M. M., Holland, B. R., Fackrell, M., & Heydar, M. (2019). Decision support model for the patient admission scheduling problem with random arrivals and departures. The 10th International Conference on Matrix-Analytic Methods in Stochastic Models. Hobart, Australia. Read online
  2. Duncan, E. (2019). The Australian Cancer Atlas: mapping reliable small-area estimates of cancer incidence and survival. GEOMED 2019. Glasgow, UK.
  3. Heydar, M., & O'Reilly, M. M. (2019). Markovian decision-support model for patient-to-ward assignment problem in a random environment. The 10th International Conference on Matrix-Analytic Methods in Stochastic Models. Hobart, Australia. Read online
  4. 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. Hobart, Australia. Read online
  5. Liquet, B. (2019). Bayesian variable selection regression for group data: application to leverage pleiotropic effect. International Workshop on Perspectives On High-dimensional Data Analysis. Uppsala, Sweden. Read online
  6. Liquet, B. (2019). PLS for Big Data: A Unified Parallel Algorithm for Regularized Group PLS. useR! 2019. Toulouse, France. Read online
  7. Liquet, B. (2019). Pleiotropic mapping for genome-wide association studies using group variable selection. EPICLIN 2019 / 26ème Journées des Statisticiens des CLCC. Toulouse, France. Read online
  8. Nguyen, G., & Peralta, O. (2019). Rate of strong convergence of stochastic fluid processes to Markov-modulated Brownian motion. The 10th International Conference on Matrix-Analytic Methods in Stochastic Models. Hobart, Australia. Read online
  9. Nguyen, N., Gunawan, D., Tran, M. - N., & Kohn, R. (2019). Long short term memory stochastic volatility. The 3rd International Conference on Econometrics and Statistics (EcoSta 2019). Taichung, Taiwan.
  10. Nguyen, N., Tran, M. - N., Kohn, R., & Nott, D. (2019). Stochastic Variational Bayes with Particle Filter. The 12th International Conference on Monte Carlo Methods and Applications. Sydney, Australia.
  11. Pollett, P. (2019). Quasi stationarity. The 10th International Conference on Matrix-Analytic Methods in Stochastic Models. Hobart, Australia. Read online
  12. Thilan, P., Peterson, E., Menendez, P., M. Caley, J., Drovandi, C., Mellin, C., & McGree, J. (2019). Optimisation of coral reef monitoring using Bayesian adaptive design methods. Bayes on the Beach 2019. Gold Coast, Australia.
  13. Wang, Y. - G., & Wu, J. (2019). Chaotic time series regression modeling using phase space reconstruction and deep neural network. The 20th INFORMS Applied Probability Society Conference. Brisbane, Australia.
  14. Wurm, M., Baird, A., Bean, N., Connolly, S., Helfgott, A., & Nguyen, G. (2019). Polyp fiction: A stochastic fluid model for the Adaptive Bleaching Hypothesis. The 10th International Conference on Matrix-Analytic Methods in Stochastic Models. Hobart, Australia. Read online

Unpublished presentation or other conference paper (where none of the above categories are met. For example, published on conference website or program.)

  1. Adams, M. P., Koh, E. J. Y., Vilas, M. P., Collier, C. J., McKenzie, L., Quiroz, M., Sisson, S., Mcdonald-Madden, E., Lambert, V. M., & O’Brien, K. R. (2019). Maximising the benefit of ecological data using Bayesian inference for ODEs. Satellite Workshop Applied^2 Probability - “Uncertainty Quantification Applications”. Brisbane, Australia.
  2. Ali, T. F., & Woodley, A. (2019). STCEC: A Remote Sensing Dataset for Identifying Spatial-Temporal Change in Homogeneous and Heterogeneous Environments. 2019 Digital Image Computing: Techniques and Applications (DICTA) 2019 Digital Image Computing: Techniques and Applications (DICTA) 8946005. Perth, Australia. Read online
  3. Asanjarani, A. (2019). Some Results on Stationary Markovian Arrival Processes. 70th Annual New Zealand Statistical Association (NZSA) conference. Dunedin, New Zealand.
  4. Aswi, A., Cramb, S., Hu, W., White, G., & Mengersen, K. (2019). Bayesian spatio-temporal conditional autoregressive localised model. ACEMS Retreat 2019. Glenelg, Australia.
  5. Aswi, A., Cramb, S., Hu, W., White, G., & Mengersen, K. (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. Gold Coast, Australia.
  6. Bednarz, T. (2019). Digital Twins, Expanded Perception and Interaction. 25th ACM Symposium on Virtual Reality Software and Technology. Sydney, Australia. Read online
  7. Bednarz, T. (2019). Expended Perception and Interaction. 16th International Conference Computer Graphics, Imaging and Visualization. Adelaide, Australia. Read online
  8. Bednarz, T. (2019). Hybrid Analytics, Digital Twins and Expanded Perception and Interaction. Sydney, Australia .Read online
  9. Bon, J. J., Lee, A., & Drovandi, C. (2019). Delayed-acceptance sequential Monte Carlo. BSU Seminar. Cambridge, UK.
  10. Bon, J. J., Lee, A., & Drovandi, C. (2019). Delayed-acceptance sequential Monte Carlo. Bayes on the Beach 2019. Gold Coast, Australia.
  11. Botha, I., Kohn, R., & Drovandi, C. (2019). Bayesian Parameter Inference for Stochastic Differential Equation Mixed Effects Models. The 12th International Conference on Monte Carlo Methods and Applications. Sydney, AustraliaRead online
  12. Botha, I., Kohn, R., & Drovandi, C. (2019). Particle Methods for Stochastic Differential Equation Mixed Effects Models. Bayes on the Beach 2019. Gold Coast, AustraliaRead online
  13. Burrage, K. (2019). Perlin noise and microfibrosis. 17th International Conference of Numerical Analysis and Applied Mathematics. Rhodes, Greece.
  14. Burrage, K. (2019). Population of models and hetereogeneity. Recent Developments in Mathematical and Computational Biomedicine. Oaxaca, MexicoRead online
  15. Burrage, K., Burrage, P., & MacNamara, S. (2019). Reflectionless models via Bessel Functions. ANZIAM Conference 2019. Nelson, New Zealand.
  16. Burrage, P. (2019). Integrated Approaches for Stochastic Chemical Kinetics. ANZIAM Conference 2019. Nelson, New Zealand.
  17. Burrage, P., Burrage, K., & MacNamara, S. (2019). Integrated Approaches for Stochastic Chemical Kinetics. 17th International Conference of Numerical Analysis and Applied Mathematics. Rhodes, Greece.
  18. Coller, J. K., Korver, S. K., Ball, I. A., Gibson, R. J., Tuke, J., Logan, R. M., Richards, A., Mead, K., Karapetis, C. S., Keefe, D. M., & Bowen, J. M. (2019). Predictors of severe gastrointestinal toxicity risk in patients treated with 5-fluorouracil-based chemotherapy: A validation study. COSA's 46th Annual Scientific Meeting, Urological cancer; Age and gender in cancer practice; Digital health in cancer. Adelaide, Australia. Read online
  19. 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. Nelson, New Zealand.
  20. 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. Brisbane, Australia.
  21. Cramb, S. M. (2019). Mapping cancer outcomes to identify and address inequities: the Australian Cancer Atlas. Data Science and Social Good Symposium. Brisbane, Australia.
  22. Cramb, S. M. (2019). The Australian Cancer Atlas. Locate19. Melbourne, Australia.
  23. Cramb, S. M., Duncan, E. W., Aitken, J. A., Mengersen, K. L., & Baade, P. D. (2019). Small-area melanoma incidence patterns across Australia: the thick and thin of it. Australasian Epidemiological Association Annual Scientific Meeting 2019. Brisbane, Australia.
  24. Cramb, S. M., Duncan, E. W., Baade, P. D., & Mengersen, K. L. (2019). Computing the Australian Cancer Atlas: getting it ‘just right’. The 12th International Conference on Monte Carlo Methods and Applications. Sydney, Australia.
  25. Dang, K. - D., Quiroz, M., Kohn, R., Tran, M. - N., & Villani, M. (2019). Efficient Bayesian inference for large data sets by HMC with energy conserving subsampling. The 10th European Seminar on Bayesian Econometrics. Scotland, UK.
  26. De Gier, J. (2019). Current distribution function for the Arndt-Heinzel-Rittenberg model. Curiosity-Driven Physics: From Algebras to Quantum Chains and Statistical Mechanics. Trieste, Italy.
  27. De Gier, J. (2019). Limit shape of the asymmetric five vertex model. ANZAMP Annual Meeting 2019. Merimbula, Australia.
  28. Drovandi, C. (2019). Efficient parameter estimation for complex simulation-based generative models. AutoStat Research Week: Frontiers in Research & Practice in Statistics. Brisbane, Australia.
  29. Drovandi, C. (2019). Robust Approximate Bayesian Inference with Synthetic Likelihood. The 12th International Conference on Monte Carlo Methods and Applications. Sydney, Australia.
  30. Duncan, E. (2019). Comparing spatial models in the presence of spatial smoothing. School of Mathematics and Statistics Seminar. University of Glasgow.
  31. Duncan, E. (2019). Novel visualisations for a digital, interactive cancer atlas. OzViz Workshop 2019. Brisbane, Australia.
  32. Duwalage, K. (2019). Forecasting daily counts of patient presentations in Australian emergency departments using statistical models with time-varying predictors. Young Statisticians Conference 2019. Canberra, Australia.
  33. Eshragh, A., Roosta, F., Nazari, A., & Mahoney, M. (2019). Big Time Series Data and Randomized Numerical Linear Algebra. The 20th INFORMS Applied Probability Society Conference. Brisbane, Australia.
  34. Fan, Y. (2019). Approximate Bayesian Inference for Potts models. The 3rd International Conference on Econometrics and Statistics (EcoSta 2019). Taichung, Taiwan.
  35. Frazier, D. T., Loaiza-Maya, R., & Martin, G. M. (2019). Focused Bayesian Prediction. The 12th International Conference on Monte Carlo Methods & Applications. Sydney, Australia: The 12th International Conference on Monte Carlo Methods & Applications
  36. Gilholm, P., Mengersen, K., & Thompson, H. (2019). Identifying latent subgroups of children with developmental delay using Bayesian sequential updating and Dirichlet process mixture modelling - poster. 40th Annual Conference of the International Society for Clinical Biostatistics. Leuven, Belgium.
  37. Gilholm, P., Mengersen, K., & 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. Gold Coast, Australia.
  38. Gunawan, D., Dang, K. - D., Quiroz, M., Kohn, R., & Tran, M. - N. (2019). Subsampling Sequential Monte Carlo for Static Bayesian Models. The 12th International Conference on Monte Carlo Methods and Applications. Sydney, Australia.
  39. Hall, P., Johnstone, I. M., Ormerod, J. T., Wand, M. P., & Yu, J. C. F. (2019). Frequentist Expectation Propagation. Statistical Methods for the Analysis of High-Dimensional and Massive Data Sets. Brisbane, Australia.
  40. Hautphenne, S. (2019). An introduction to Markovian binary trees and their applications. Phylomania 2019. Hobart, Australia.
  41. Hautphenne, S. (2019). The Markovian binary tree applied to demography and conservation biology. Macquarie University: Departmental colloquium. Sydney, Australia.
  42. Hautphenne, S., Braunsteins, P., & Abril, C. Minuesa. (2019). Inference in Population-Size-Dependent Branching Processes. Seventh Wellington Workshop in Probability and Mathematical Statistics. Wellington, New Zealand.
  43. Herath, S. (2019). Name-like Numbers for Simulating Names in Entity Resolution. Data Science Down Under 2019. Newcastle, Australia.
  44. 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. Würzburg, Germany.
  45. Herath, S. (2019). Name-like Numbers for Simulating Names in Entity Resolution. Young Statisticians Conference 2019. Canberra, Australia.
  46. Holloway, J. (2019). Beta distributions and random forest for better forest monitoring. Bayes on the Beach 2019. Brisbane, Australia.
  47. Holloway, J. (2019). Environmental Data in the Development of SDG indicators. 62nd ISI World Statistics Congress. Kuala Lumpur, Malaysia.
  48. Hu, S., Li, Y., Ingham, A., Hurley, G. B., Gonzalez, E. G., & Wang, Y. - G. (2019). Comparison of Machine Learning Algorithms in Classification of Grazing Behaviour in Sheep. The 20th INFORMS Applied Probability Society Conference. Brisbane, Australia.
  49. Hyndman, R. J. (2019). Feature-based forecasting algorithms for large collections of time series. Statistical Methods for the Analysis of High-Dimensional and Massive Data Sets. Brisbane, Australia. Read online
  50. Jahan, F., & Mengersen, K. (2019). Bayesian Empirical Likelihood Spatial Model applying Leroux Structure. Bayes on the Beach 2019. Gold Coast, Australia.
  51. Jahan, F., & Mengersen, K. (2019). Bayesian Empirical Likelihood Spatial Model applying Leroux Structure. Young Statisticians Conference 2019. Canberra, Australia.
  52. Jahan, F., Duncan, E., Cramb, S., Baade, P., & Mengersen, K. (2019). Multivariate Bayesian meta-analysis model to analyse estimated cancer incidence. ACEMS Students and ECR Retreat. Glenelg, Australia.
  53. Jahan, F., Duncan, E., Cramb, S., Mengersen, K., & Baade, P. (2019). Augmeting Disease Maps: a Bayesian meta-analysis approach. Young Statisticians Conference 2019. Canberra, Australia.
  54. 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. Brisbane, Australia.
  55. 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". Eindhoven, The Netherlands.
  56. Jony, R. Islam, Woodley, A., & Perrin, D. (2019). Flood Detection in Social Media Images using Visual Features and Metadata. 2019 Digital Image Computing: Techniques and Applications (DICTA), 8946007. Perth, Australia. Read online
  57. Kekulthotuwage Don, S. P. (2019). Accounting for uncertainty of predator-prey systems into mathematical model formulations. ESA19: the 2019 Conference of the Ecological Society of Australia. Launceston, Australia.
  58. Kekulthotuwage Don, S. P. (2019). Cost-effective predator harvesting per dollar. Student Conference on Conservation Science. Brisbane, Australia.
  59. Kobakian, S. (2019). An Australian alternative to choropleth maps; visualising geo-spatial disease data. WOMBAT19. Melbourne, Australia. Read online
  60. Kobakian, S. (2019). Maps, hexagons and life in Australia. Young Statisticians Conference 2019. Canberra, Australia.
  61. Krzysik, O., De Sterck, H., MacLachlan, S., & Friedhoff, S. (2019). Selecting coarse-grid operators for Parareal and MGRIT. 19th Copper Mountain Conference on Multigrid Methods. Copper Mountain, USA.
  62. Krzysik, O., De Sterck, H., MacLachlan, S., & Friedhoff, S. (2019). Space-time reduction methods for linear PDEs. SIAM Conference on Computational Science and Engineering (CSE19). Washington, USA.
  63. Kunnel, A., Glonek, G., & Tuke, J. (2019). Can Hypercubes Overcome the Issue of High Dimensionality in Gene Expression Data for Sepsis. Young Statisticians Conference 2019. Canberra, Australia".
  64. Latouche, G., Nguyen, G., & Peralta, O. (2019). Convergence of bivariate flip–flop processes. Queues, Modelling, and Markov Chains: A Workshop Honouring Prof. Peter Taylor. Mount Tamborine, Australia.
  65. Lawson, B. A. J., Burrage, K., Burrage, P., Turner, I., Santos, R. W. dos, Jakes, D., Bueno-Orovio, A., Rodriguez, B., & Drovandi, C. C. (2019). Mathematically Coping with Microfibrosis. Mathematical and Computational Biology Seminar Series. Melbourne, Australia".
  66. Lawson, B. A. J., Jakes, D., Burrage, K., Burrage, P., Drovandi, C. C., & Bueno-Orovio, A. (2019). Perlin noise for automatic generation of complex spatial patterns: an application to cardiac fibrosis. ANZIAM Conference 2019. Nelson, New Zealand".
  67. Li, D., Clements, A., & Drovandi, C. (2019). Efficient Bayesian Estimation for GARCH-type Models via Sequential Monte Carlo. The 12th International Conference on Monte Carlo Methods and Applications. Sydney, Australia.
  68. Li, D., Clements, A., & Drovandi, C. (2019). Efficient Bayesian estimation for GARCH-type models via Sequential Monte Carlo. Bayes on the Beach 2019. Gold Coast, Australia.
  69. Liquet, B. (2019). Leverage pleiotropic effects from genome-wide association studies using both frequentist and Bayesian sparse group models. Bayes on the Beach 2019. Gold Coast, Australia. Read online
  70. Liquet, B. (2019). Variable Selection and Dimension Reduction methods for high dimensional and Big-Data Set. Seminar at the Department of Statistics, The University of Auckland. Auckland, New Zealand.
  71. Liquet, B. (2019). Variable Selection and Dimension Reduction methods for high dimensional and Big-Data Set. Seminar at the School of Mathematics and Statistics, UNSW Sydney. Sydney, Australia. Read online
  72. Loaiza-Maya, R., Martin, G. M., & Frazier, D. T. (2019). Focused Bayesian Prediction. The 39th International Symposium on Forecasting. Thessaloniki, Greece.
  73. Maestrini, L., Aykroyd, R. G., & Wand, M. P. (2019). Compartmentalisation of Variational Approximate Inference for Inverse Problems Models. 3rd International Conference on Econometrics and Statistics (EcoSta 2019). Taichung, Taiwan.
  74. Maestrini, L., Aykroyd, R. G., & Wand, M. P. (2019). Streamlined Variational Inference for Inverse Problems Models. The 12th International Conference on Monte Carlo Methods and Applications. Sydney, Australia.
  75. Maestrini, L., Tan, L. S. L., & Wand, M. P. (2019). Double-loop expectation propagation for statistical models. 2019 ACEMS Enabling Algorithms Theme Symposium. Sydney, Australia.
  76. Martin, G. M. (2019). Focused Bayesian Prediction. 13th RCEA Bayesian Econometrics Workshop. Larnaca, Cyprus.
  77. Martin, G. M. (2019). Focused Bayesian Prediction. AutoStat Research Week: Frontiers in Research & Practice in Statistics. Brisbane, Australia".
  78. Martin, G. M. (2019). Looking into the Future with the Reverend! The Future of Bayesian Forecasting. The 39th International Symposium on Forecasting. Thessaloniki, Greece.
  79. McLaughlin, C., Hutson, H., De Vine, L., Woodley, A., Geva, S., Chappell, T., Kelly, W., Boles, W., & Perrin, D. (2019). Change Detection over the State of Queensland using High Resolution Planet Satellite Mosaics. 2019 Digital Image Computing: Techniques and Applications (DICTA), 8945942. Perth, Australia. Read online
  80. Mengersen, K. (2019). Bayesian Learning for Decision Making in the Big Data Era. Bayes on the Beach 2019. Gold Coast, Australia.
  81. Mengersen, K. (2019). Bayesian Statistical Analysis of Large Images. Newcastle, Australia.
  82. Mengersen, K. (2019). The Challenges, Discoveries and Examples of ABC. The 12th International Conference on Monte Carlo Methods and Applications. Sydney, Australia.
  83. Mengersen, K. (2019). Virtual reality meets data science. OzViz 2019. Brisbane, Australia. Read online
  84. Mengersen, K. (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. Makassar, Indonesia.
  85. Moka, S. Babu, Kroese, D. P., & Juneja, S. (2019). Unbiased Estimation of the Reciprocal Mean for Non-negative Random Variables. The20th INFORMS Applied Probability Society Conference. Brisbane, Australia.
  86. Moka, S. Babu, Kroese, D. P., & Juneja, S. (2019). Unbiased Estimation of the Reciprocal Mean for Non-negative Random Variables with Applications. The 12th International Conference on Monte Carlo Methods and Applications. Sydney, Australia.
  87. Moores, M. T., Carson, J., Lawrence, E., Bhat, S., Girolami, M., & Myers, K. (2019). Statistics from Mars: Bayesian signal processing for Raman spectroscopy. Bayes on the Beach 2019. Gold Coast, Australia. Read online
  88. Moores, M. T., Nicholls, G. K., Pettitt, A. N., & Mengersen, K. (2019). Bayesian Indirect Likelihood for the Potts Model. The 12th International Conference on Monte Carlo Methods and Applications. Sydney, Australia. Read online
  89. Nguyen, G. (2019). Convergence of stochastic processes. Third Victorian Research Students' Meeting in Probability and Statistics. Melbourne, Australia.
  90. Nguyen, G., & Peralta, O. (2019). Rate of strong convergence of stochastic fluid processes to Markov-modulated Brownian motion. The 20th INFORMS Applied Probability Society Conference. Brisbane, Australia.
  91. Niknami, B., & Taylor, P. (2019). Modelling Double Auctions with Dynamic Supply and Demand. The 20th INFORMS Applied Probability Society Conference. Brisbane, Australia. Read online
  92. Peterson, E., Hoef, J. Ver, Hooten, M., Hanks, E., & Fortin, M. - J. (2019). Estimating parameters of landscape resistance using spatial autoregressive models. The 10th International Association for Landscape Ecology World Congress. Milan, Italy.
  93. Peterson, E., Mengersen, K., Vercelloni, J., & Brown, R. (2019). Combining citizen science and professional monitoring data to conserve and sustainably use marine resources. 62nd ISI World Statistics Congress. Kuala Lumpur, Malaysia.
  94. Peterson, E., Mengersen, K., Vercelloni, J., & Brown, R. (2019). Virtual Reef Diver. International Workshop on Spatial Statistics. Creswick, Australia.
  95. Pollett, P. K. (2019). Infinite-patch metapopulation models: branching, convergence and chaos. 6th Conference on Mathematical Models in Ecology and Evolution. Lyon, France. Read online
  96. Priddle, J. W., Sisson, S. A., & Drovandi, C. C. (2019). Efficient Bayesian synthetic likelihood with whitening transformations. Bayes on the Beach 2019. Gold Coast, Australia.
  97. Quella, T. (2019). Symmetry protected topological phases in one dimension: Beyond groups. Topological Phases of Interacting Quantum Systems. Oaxaca, Mexico.
  98. Rajapaksha, S., Garoni, T., & Zhang, J. (2019). Approximating Link Travel Time Distributions. 63rd Annual Meeting of the Australian Mathematical Society. Melbourne, Australia.
  99. Roughan, M. (2019). Space in Narrative Networks. The Fourth Annual Australian Social Network Analysis Conference 2019. Adelaide, Australia.
  100. Salomone, R., South, L. F., Drovandi, C. C., & Kroese, D. P. (2019). Unbiased and Consistent Nested Sampling via Sequential Monte Carlo. The 12th International Conference on Monte Carlo Methods and Applications. Sydney, Australia. Read online
  101. Shausan, A., Mengersen, K., Drovandi, C., & Aaskov, J. (2019). Minimising Dengue Spread Using TIP Therapy. Bayes on the Beach 2019. Gold Coast, Australia.
  102. Shausan, A., Mengersen, K., Drovandi, C., & Aaskov, J. (2019). Minimising Dengue Spread by Transmissible Interfering Particles. Young Statistician Conference 2019. Canberra, Australia.
  103. Shausan, A., Mengersen, K., Drovandi, C., & Aaskov, J. (2019). Minimising the severity of dengue Serotype 1 infection by transmissible interfering particles. ACEMS Node Planning Day. Brisbane, Australia.
  104. Sisson, S. A. (2019). New models for symbolic data analysis. 62nd ISI World Statistics Congress. Kuala Lumpur, Malaysia.
  105. Sisson, S. A. (2019). The past, present and future of Monte Carlo Methods. The 12th International Conference on Monte Carlo Methods and Applications. Sydney, Australia.
  106. Smith-Miles, K. (2019). Party Tricks with Numerical Linear Algebra and the Quest for Trust. Data Science Down Under 2019. Newcastle, Australia.
  107. South, T., Roughan, M., & Mitchell, L. (2019). Critical transition of eigen-centrality of artists as a function of popularity. The Fourth Annual Australian Social Network Analysis Conference 2019. Adelaide, Australia.
  108. Talagala, P. Dilini. (2019). Anomaly Detection in R. useR! 2019. Toulouse, France".
  109. Talagala, T. (2019). Peeking inside FFORMS: Feature-based FORecast Model Selection. The 39th International Symposium on Forecasting. Thessaloniki, Greece. Read online
  110. Taylor, P. (2019). Block arrivals in the Bitcoin blockchain. 16th Workshop on Algorithms and Models for the Web Graph (WAW 2019). Brisbane, Australia
  111. Taylor, P. (2019). Some Thoughts About a Distributed Solution of the PageRank Equation. Data Science Down Under 2019. Newcastle, Australia
  112. Tierney, N., & Cook, D. (2019). Exploring and understanding the individual experience from longitudinal data, or .. How to make better spaghetti (plots). OzViz 2019. Brisbane, Australia. Read online
  113. Tierney, N., & Cook, D. (2019). Exploring and understanding the individual experience from longitudinal data, or .. How to make better spaghetti (plots). Research School of Finance, Actuarial Studies & Statistics Seminar. Canberra, Australia.
  114. Tierney, N., Cook, D., & McBain, M. (2019). Are we tere yet? Testing the effectiveness of graphics as MCMC diagnostics. John Hopkins University Seminar. Baltimore, USA.
  115. Wand, M. (2019). Streamlined Variational Inference for Random Effects Models. Data Science Down Under 2019. Newcastle, Australia.
  116. Wand, M. (2019). Variational Message Passing for Elaborate Response Regression Models. AutoStat Research Week: Frontiers in Research & Practice in Statistics. Brisbane, Australia.
  117. Wand, M. (2019). Variational Message Passing for Elaborate Response Regression Models. Joint Statistical Meetings 2019. Colorado, USA.
  118. Wang, S., Zhang, L., & Thompson, R. G. (2019). A Study of Loading Zone Usage in Melbourne's Central City Areas. The 24th International Conference of Hong Kong Society for Transportation Studies. Hong Kong, Hong Kong.
  119. Whyte, J. M. (2019). An introduction to the testing of model structures for global a priori identifiability (with examples drawn from Plasmodium falciparum malaria modelling). Influencing Public Health Policy with Data-informed Mathematical Models. Creswick, Australia.
  120. Wu, P. Pao- Yen, Ruggeri, F., & Mengersen, K. (2019). Observational Uncertainty in Bayesian Networks and State Space Models. Bayes on the Beach 2019. Gold Coast, Australia.
  121. Wurm, M., Bean, N., & Nguyen, G. (2019). Polyp fiction: a stochastic fluid flow model for coral-algal symbiosis on the Great Barrier Reef. The 20th INFORMS Applied Probability Society Conference. Brisbane, Australia.
  122. Yu, J. (2019). Fast and Accurate Frequentist Generalised Linear Mixed Model Analysis via Expectation Propagation. 2019 ACEMS Enabling Algorithms Theme Symposium. Sydney, Australia.

Publicly available software

Software and computing packages

  1. Delaigle, A., Hyndman, T., & Wang, T. (2019). deconvolve: Deconvolution tools for measurement error problems.” Read online
  2. Fan, X. (2019). Sparti. Read online
  3. Froese, J. G., Pearse, A. R., & Hamilton, G. S. (2019). riskmapr. Read online
  4. Kang, Y., Li, F., & Hyndman, R. J. (2019). GRATIS: GeneRAting TIme Series with diverse and controllable characteristics .Read online
  5. Kobakian, S., & Cook, D. (2019). sugarbag. Read online
  6. Laub, P. J. (2019). EMpht.jl. Read online
  7. Lawson, B. A. J., Santos, R. W. dos, & Turner, I. (2019). Two-dimensional monodomain solver. Read online
  8. Pearse, A., Peterson, E. E., McGree, J., Leigh, C., Hoef, J. Ver, & Som, N. (2019). SSNdesign – an R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks. Read online
  9. South, T. (2019). ProcessEntropy. Read online
  10. Talagala, P. Dilini, Hyndman, R. J., & Smith-Miles, K. (2019). oddstream: Outlier Detection in Data Streams. Read online
  11. Talagala, P. Dilini, Hyndman, R. J., & Smith-Miles, K. (2019). stray: Anomaly Detection in High Dimensional and Temporal Data. Read online
  12. Tsuchida, R. (2019). Code to supplement "Richer priors for infinitely wide multi-layer perceptrons". Read online

Data sets

  1. Talagala, T. (2019). tea: R package for tea exporting countries. Read online

Technical reports and unrefereed outputs

Unpublished Reports
  1. Bailleres, H., Lee, D., Kumar, C., Psaltis, S., Hopewell, G., Brancheriau, L., Turner, I., Carr, E., & Farrell, T. (2019). Improving returns from southern pine plantations through innovative resource characterisation. Read online
  2. Cameron, J., Kennedy, D., & Mengersen, K. (2019). Executive Summary of the Benchmarking Project.
  3. Kandanaarachchi, S., Hyndman, R. J., & Smith-Miles, K. (2019). Early classification of spatio-temporal events using partial information. Read online
  4. Panagiotelis, A., Gamakumara, P., Athanasopoulos, G., & Hyndman, R.. (2019). Forecast reconciliation: A geometric view with new insights on bias correction. Read online
  5. Ryan, L., Chen, V., Beavan, A., Speilmann, J., Sisson, S., & Kohn, R. (2019). A longitudinal analysis of the developmental trajectories of domain specific and domain generic abilities in high-level football players.
  6. Shausan, A., Mengersen, K., Drovandi, C., & Aaskov, J. (2019). DARPA dengue project.
  7. Ullah, I., Mengersen, K., Hyndman, R. J., & McGree, J. (2019). Detection of cybersecurity attacks through analysis of web browsing activities using principal component analysis. Read online