Our people

2019

Wikle, C.K., Zammit-Mangion, A., and Cressie, N. (2019). Spatio-Temporal Statistics with R. Chapman & Hall/CRC, Boca Raton, FL (380 pp.).

2011

Cressie, N. and Wikle, C.K. (2011). Statistics for Spatio-Temporal Data. Wiley, Hoboken, NJ (588 pp.).

1993

Cressie, N. (1993). Statistics for Spatial Data, rev. edn. Wiley, New York, NY (900 pp.). (Original edition, 1991. Paperback edition in the Wiley Classics Library: Wiley, Hoboken, NJ, 2015).

1988

Read, T.R.C. and Cressie, N. (1988). Goodness-of-Fit Statistics for Discrete Multivariate Data. Springer, New York, NY (211 pp.).

2019

Wikle, C.K., Zammit-Mangion, A., Cressie, N. (2019). Spatio-Temporal Statistics with R. Chapman & Hall/CRC, Boca Raton, FL.

2013

Zammit Mangion, A., Dewar, M., Kadirkamanathan, V., Flesken, A., and Sanguinetti, G. (2013). Modeling Conflict Dynamics using Spatio-temporal Data. London, UK: Springer.

2020

Zammit-Mangion, A., and Wikle, C.K. (2020). Deep integro-difference equation models for spatio-temporal forecasting, in press with Spatial Statistics.

Zammit-Mangion, A., and Cressie, N. (2020). FRK: An R package for spatial and spatio-temporal prediction with large datasets, in press with Journal of Statistical Software.

2018

Zammit-Mangion, A., and Rougier, J.C. (2018). A sparse linear algebra algorithm for fast computation of prediction variances with Gaussian Markov random fields, Computational Statistics & Data Analysis, 123, pp. 116-130.

Zammit-Mangion, A., Cressie N., and Shumack, C. (2018). On statistical approaches to generate Level 3 products from statistical remote sensing retrievals, Remote Sensing, 10(1), 155.

2016

Cseke, B., Zammit-Mangion, A., Sanguinetti, G., and Heskes, T. (2016). Sparse approximations in spatio-temporal point-process models, Journal of the American Statistical Association, 111(516), pp. 1746–1763.

Cressie, N., and Zammit-Mangion, A. (2016). Multivariate spatial covariance models: A conditional approach, Biometrika, 103(4), pp. 915–935.

Zammit-Mangion, A., Cressie, N., and Ganesan, A.L. (2016). Non-Gaussian bivariate modelling with application to atmospheric trace-gas inversion, Spatial Statistics, 18(A), pp. 194–220.

2015

Zammit Mangion, A., Rougier, J., Schoen, N., Lindgren, F., and Bamber, J. (2015). Multivariate spatio-temporal modelling for assessing Antarctica's present-day contribution to sea-level rise. Environmetrics, 26(3), 159-177.

2012

Zammit Mangion, A., Dewar, M., Kadirkamanathan, V., and Sanguinetti, G. (2012). Point process modelling of the Afghan War Diary. Proceedings of the National Academy of Sciences (PNAS), 109(31), 12414-12419. Awarded the Cozzarelli Prize from the National Academy of Sciences.

2011

Zammit Mangion, A., Yuan, K., Kadirkamanathan, V., and Sanguinetti, G. (2011). Online variational inference for state-space models with point-process observations. Neural Computation, 23(8), 1967-1999.

2020

Härkönen, T., Roininen, L., Moores, M.T., and Vartiainen, E.M. (2020) Bayesian quantification for coherent anti-Stokes Raman scattering spectroscopy. Journal of Physical Chemistry B, 124(32), 7005-7012.

Moores, M.T., Nicholls, G.K., Pettitt, A.N., and Mengersen, K. (2020). Scalable Bayesian inference for the inverse temperature of a hidden Potts model. Bayesian Analysis, 15(1), 1-27.

Moores, M.T., Pettitt, A.N., and Mengersen, K. (2020). Bayesian computation with intractable likelihoods. In Mengersen, K., Pudlo, P. & Robert, C.P. (eds.), Case Studies in Applied Bayesian Data Science, vol. 2259, Springer Nature, Cham, Switzerland.

2018

Noonan, J., Asiala, S.M., Grassia, G., MacRitchie, N., Gracie, K., Carson, J., Moores, and M., et al. (2018). In vivo multiplex molecular imaging of vascular inflammation using surface-enhanced Raman spectroscopy. Theranostics, 8(22), 6195.

Drovandi, C.C., Moores, M.T., and Boys, R.J. (2018). Accelerating pseudo-marginal MCMC using Gaussian processes. Computational Statistics and Data Analysis, 118, 1-17.

2016

Gracie, K., Moores, M., Smith, W. E., Harding, K., Girolami, M., Graham, D., and Faulds, K. (2016). Preferential attachment of specific fluorescent dyes and dye labelled DNA sequences in a SERS multiplex. Analytical Chemistry, 88(2), 1147-1153.

Hargrave, C., Mason, N., Guidi, R., Miller, J. A., Becker, J., Moores, M., Mengersen, K.,Poulsen, M., and Harden, F. (2016). Automated replication of cone beam CT‐guided treatments in the Pinnacle³ treatment planning system for adaptive radiotherapy. Journal of Medical Radiation Sciences, 63(1), 48-58.

2015

Moores, M.T., Drovandi, C.C, Mengersen, K., and Robert, C.P (2015). Pre-processing for approximate Bayesian computation in image analysis. Statistics and Computing, 25, 22-33.

Moores, M.T., Hargrave, C.E., Deegan, T., Poulsen, M., Harden, F., and Mengersen, K. (2015). An external field prior for the hidden Potts model with application to cone-beam computed tomography. Computational Statistics and Data Analysis, 86, 27-41.

Falk, M. G., Alston, C. L., McGrory, C. A., Clifford, S., Heron, E. A., Leonte, D., Moores, M., Walsh, C. D., Pettitt, A.N., and Mengersen, K. (2015). Recent Bayesian approaches for spatial analysis of 2-D images with application to environmental modelling. Environmental and Ecological Statistics, 22(3), 571-600.

Ng T.L.J., Murphy, T.B., Model-based Clustering of Count Processes, Journal of Classification, accepted.

Yang L., Ng T.L.J., Smyth B., Dong R. (2020), HTML: Hierarchical Transformer-based Multi-task Learning for Volatility Prediction, Proceedings of The Web Conference 2020, 441-451.

2019

Ng T.L.J., Murphy, T.B. (2019), Estimation of the intensity function of an inhomogeneous Poisson process with a change‐point, Canadian Journal of Statistics, 47 (4), 604-618.

Ng T.L.J., Murphy, T.B. (2019), Generalized Random Dot Product graph (2019), Statistics & Probability Letters, 148, 143 – 149.

Fosdick, B.K., McCormick, T.H., Murphy, T.B., Ng T.L.J., and Westling, T. (2019), Multiresolution network models. Journal of Computational and Graphical Statistics, 28 (1), 185-196.

Yang L., Xu Y., Ng T.L.J., Dong R., (2019), Leveraging BERT to Improve the FEARS Index for Stock Forecasting, Proceedings of the First Workshop on Financial Technology and Natural Language Processing (FinNLP@IJCAI 2019), 54-60.

2017

Yang L., Ng T.L.J., Mooney C., Dong R., (2017), Multi-level Attention-Based Neural Networks for Distant Supervised Relation Extraction, Proceedings of Irish Conference on Artificial Intelligence and Cognitive Science, 206-218.

2020

Gunawan, D., Khaled, M., and Kohn, R. (2020). Mixed marginal copula modeling. Journal of Business and Economic Statistics, 38(1), 137-147.

Gunawan, D., Hawkins, G., Tran, M. N., Kohn, R., and Brown, S. (2020). New estimation approaches for the hierarchical linear ballistic accumulator model. In press with Journal of Mathematical Psychology.

Lander, D., Gunawan, D., Griffiths, W. E., and Chotikapanich, D. (2020). Bayesian assessment of Lorenz and stochastic dominance. In press with Canadian Journal of Economics.

Gunawan, D., Panagiotelis, A., Griffiths, W. E., and Chotikapanich, D. (2020). Bayesian weighted inference from surveys. In press with Australian and New Zealand Journal of Statistics.

Mendes, E., Carter, C., Gunawan, D., and Kohn, R. (2020). A flexible particle Markov chain Monte Carlo method. In press with Statistics and Computing.

Chin, V., Gunawan, D., Fiebig, D. G., Kohn, R., and Sisson, S. (2020). Efficient data augmentation for multivariate probit models with panel data: an application to general practitioner decision making about contraceptives. In press with Journal of Royal Statistical Society Series C.

Tran, M. N., Scharth, M., Gunawan, D., Kohn, R., Brown, S., and Hawkins, G. E. (2020). Robustly estimating marginal likelihood for cognitive models via importance sampling. In press with Behavior Research Methods.

Wall, L., Gunawan, D., Brown, S., Tran, M. N., Kohn, R., and Hawkins, G. E. (2020). Identifying relationships between cognitive processes across tasks, contexts, and time. In press with Behavior Research Methods.

2019

Gunawan, D., Tran, M. N., Suzuki, K., Dick, J., and Kohn, R. (2019). Computationally efficient bayesian estimation of high dimensional archimedian copulas with discrete and mixed margins. Statistics and Computing, 29, 933-946.

2018

Gunawan, D., Griffiths, W. E., and Chotikapanich, D. (2018). Bayesian inference for health inequality and welfare using qualitative data. Economics Letters, 162, 76-80.

2019

Bertolacci, M., Cripps, E., Rosen, O., Lau, J. W., S. Cripps. (2019). Climate inference on daily rainfall across the Australian continent, 1876–2015. Annals of Applied Statistics, 13(2), pp 683–712. doi: 10.1214/18-AOAS1218.

2016

Bertolacci, M., Cripps, E., Cripps, S., Lau, J. W. (2016). Bayesian mixture models for multivariate time series with an application to Australian rainfall data. Neural Information Processing Systems Time Series Workshop. Presented a poster at this workshop.

2007

Wirth, A., Bertolacci, M. (2007). Are approximation algorithms for consensus clustering worthwhile? SIAM International Conference on Data Mining.

2006

Wirth, A., Bertolacci, M. (2006). New algorithms research for first year students, 11th Annual ACM Conference on Innovation and Technology in Computer Science Education (ITICSE ’06), pp 128–32.

2022

Zheng, X., Kottas, A., & Sansó, B. (2022). On construction and estimation of stationary mixture transition distribution models. Journal of Computational and Graphical Statistics, 31(1), 283-293.

2021

Zheng, X., Kottas, A., & Sansó, B. (2021). Bayesian geostatistical modeling for discrete-valued processes. arXiv preprint arXiv:2111.01840.

2021

Zheng, X., Kottas, A., & Sansó, B. (2021). Nearest-neighbor mixture models for non-gaussian spatial processes. arXiv preprint arXiv:2107.07736.

2020

Zhang, B. and Cressie, N. (2020). Bayesian spatio-temporal modeling of Arctic sea ice extent. Bayesian Analysis, 15, 605-631.

2019

Zhang, B., Sang, H., and Huang, J. Z. (2019). Smoothed full-scale approximation of Gaussian process models for computation of large spatial datasets. Statistica Sinica, 29, 1711-1737.

Zhang, B., Cressie, N., and Wunch, D. (2019). Inference for errors-in-variables models in the presence of systematic errors with an application to a satellite remote sensing campaign. Technometrics, 61, 187-201.

Zhang, B. and Cressie, N. (2019). Estimating spatial changes over time of Arctic sea ice using hidden 2x2 tables. Journal of Time Series Analysis, 40, 288-311.

2017

Zhang, B., Cressie, N., and Wunch, D. (2017). Statistical properties of atmospheric greenhouse gas measurements looking down from space and looking up from the ground. Chemometrics and Intelligent Laboratory Systems, 162, 214-222.

Cressie, N., Burden, S., Shumack, C., Zammit-Mangion, A., and Zhang, B. (2017). Environmental Informatics. Wiley StatsRef : Statistics Reference Online, pp. 1-8 (doi:10.1002/9781118445112.stat07717.pub2).

2015

Zhang, B., Konomi, B. A., Sang, H., Karagiannis, G., and Lin, G (2015). Full scale multi-output Gaussian process emulator with nonseparable auto-covariance functions. Journal of Computational Physics, 300, 623-642.

Zhang, B., Sang, H., and Huang, J.Z. (2015). Full-scale approximations of spatio-temporal covariance models for large datasets. Statistica Sinica, 25, 99-114.

2020

Cao, Y., and Veitch, D. (2020). Toward trusted time: remote server vetting and the misfiring heart of internet timing. IEEE/ACM Transactions on Networking. pp. 1-13. 10.1109/TNET.2020.2977024.

2019

Cao, Y., and Veitch D. (2019).  Where on earth are the best-50 time servers?. In Choffnes D., Barcellos M. (eds) Passive and Active Measurement. PAM 2019. Lecture Notes in Computer Science, vol. 11419. Springer

2018

Cao, Y., and Veitch, D. (2018). Network timing, weathering the 2016 leap second, IEEE INFOCOM 2018 - IEEE Conference on Computer Communications, pp. 1826-1834.