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Professor Nigel Bean
Associate Investigator
The University of Adelaide
Professor Nigel Bean is internationally known for his research in stochastic modelling. He completed his B.Sc. (Hons) in Applied Mathematics at the University of Adelaide in 1988 and his PhD at the University of Cambridge in 1993. Since then he has been at the University of Adelaide in roles varying from a Postdoctoral fellow, Lecturer, Senior Lecturer, Director of TRC Mathematical Modelling – a commercially funded contract research and consulting centre with a turnover then of over $750,000 – and then was appointed Chair of Applied Mathematics in 2004. Since then, he has been continuously on the School Management Committee and Head of Applied Mathematics. He has also been Director of Research (2011 - 2016) and Deputy Head of School (2004 - 06, 2012 -).
The quality of Professor Bean’s research has been recognised through the award of the 2001 JH Mitchell Medal by ANZIAM and the 2003 PAP Moran Medal by the Australian Academy of Science. Professor Bean has published about 91 scientific articles and has been awarded 8 ARC awards (Large Grants, Discovery Projects and a Linkage Grant). He has supervised twenty-three successful PhD students, six successful MPhil students, and is currently supervising nine more PhD students and six more MPhil students.
Professor Bean has been very active in national leadership roles. From 2008-2010, he was on the Board of the Federation of the Australian Scientific and Technological Societies as the Mathematical Sciences Cluster Representative and on the Board of the Australian Mathematical Sciences Institute (AMSI) from 2014 - 2016. In 2011 and 2012 he was a member of the ACARA Australian Curriculum: Mathematics Advisory Panel. Professor Bean was also Co-Chair of the Sub-committee for Mathematics and statistics research in universities and related institutions and a member of the Steering Committee, Mathematical Sciences Decadal Plan (2012 -- 2016), as well as being a member of the Steering Committee. Professor Bean is Deputy Chair of the AMSI Industry/Mathematical Sciences Engagement Task Force and a member of the AMSI Industry Advisory Committee.
Research Interests:
Markov chain
Matrix analytic methods
Optimisation
Queuing theory
Stochatic Modelling
Qualifications:
PhD (Cantab) 1993
Projects
Publications
Invited talks, refereed proceedings and other conference outputs
Wurm, M., Bean N. G., & 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.
Glonek, M., Tuke J., Mitchell L., & Bean N. G.
(2019). GLaSS: Semi-supervised Graph Labelling with Markov Random Walks to Absorption.
(Aiello, L. Maria, Cherifi C., Cherifi H., Lambiotte R., Lió P., & Rocha L. M., Ed.).Complex Networks and Their Applications. 812, 304-315. doi: 10.1007/978-3-030-05411-3_25
Wurm, M., Baird A., Bean N. G., 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. 65.
Bean, N. G., Nielsen B. Friis, Nguyen G. T., & Peralta O.
(2019). A fluid flow model with RAP components.
Matrix Analytic Methods in Stochastic Models. 10,
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.
Samuelson, A., O'Reilly MM., & 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. 53-57.
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.
Glenny, V., Tuke J., Bean N. G., & 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. 61 - 70. doi: 10.18653/v1/W19-2508
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. 292 - 296. doi: 10.18653/v1/S19-2050
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".
Mathews, P., Gray C., Mitchell L., Nguyen G., & Bean N. G.
(2018). SMERC: Social media event response clustering using textual and temporal information.
2018 IEEE International Conference on Big Data (Big Data). 8622082. doi: 10.1109/BigData.2018.8622082
TARCA, SILVIO., Roughan M., Etugrul N., & Bean N. G.
(2017). Dispatchability of Wind Power with Battery Energy Storage in South Australia.
1st International Conference on Energy and Power, RMIT Univ, Melbourne, AUSTRALIA, 14 Dec 2016 - 16 Dec 2016. Energy Procedia, 1-7. doi: 10.1016/j.egypro.2017.03.131
Bean, N. G., Lewis A., & Nguyen G.
(2017). Regime-switching models for electricity pricing in SA market.
INFORMS Applied Probability Society.
Mathews, P., Mitchell L., Nguyen G., & Bean N. G.
(2017). The nature and origin of heavy tails in retweet activity.
8th International Workshop on Modelling Social Media: Machine Learning and AI for Modelling and Analysing Social Media, Perth, 03 Apr 2017 - 07 Apr 2017. 1493-1498. doi: 10.1145/3041021.3053903
Bean, N. G., Nguyen G., O'Reilly MM., & Sunkara V.
(2016). A numerical framework for computing the limiting distribution of a stochastic fluid-fluid process.
The Ninth International Conference on Matrix-Analytic Methods in Stochastic Models.
Samuelson, A., O'Reilly MM., & Bean N. G.
(2016). Classification of Generators for Stochastic Fluid Models: New Riccati equation for Psi.
The Ninth International Conference on Matrix-Analytic Methods in Stochastic Models.
Bowden, R., Roughan M., & Bean N. G.
(2014). COLD.
The 10th ACM International on Conference on emerging Networking Experiments and Technologies. doi: 10.1145/2674005.2675012
Journal Articles
Teo, M., Bean N. G., & Ross J. V.
(2021). Optimised prophylactic vaccination in metapopulations.
Epidemics. 34, 100420. doi: 10.1016/j.epidem.2020.100420
Crotty, S. M., Minh B. Quang, Bean N. G., Holland B. R., Tuke J., Jermiin L. S., et al.
(2020). GHOST: Recovering Historical Signal from Heterotachously Evolved Sequence Alignments.
(Smith, S., Ed.).Systematic Biology. 69(2), 249-264. doi: 10.1093/sysbio/syz051
Fitzgerald, S. Paul, Bean N. G., Falhammar H., & Tuke J.
(2020). Clinical Parameters Are More Likely to Be Associated with Thyroid Hormone Levels than with Thyrotropin Levels: A Systematic Review and Meta-Analysis.
Thyroid. 30(12), 1695 - 1709. doi: 10.1089/thy.2019.0535
Tuke, J., Nguyen A., Nasim M., Mellor D., Wickramasinghe A., Bean N. G., et al.
(2020). Pachinko Prediction: A Bayesian method for event prediction from social media data.
Information Processing & Management. 57(2), 102147. doi: 10.1016/j.ipm.2019.102147
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. doi: 10.14814/phy2.14153
Bean, N. G., Lewis A., & Nguyen G.
(2019). Estimation of Markovian-regime-switching models with independent regimes.
arXiv. arXiv:1906.07957v1.
Glonek, M., Tuke J., Mitchell L., & Bean N. G.
(2019). Semi-supervised graph labelling reveals increasing partisanship in the United States Congress.
arXiv. arXiv:1904.01153v2.
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. doi: 10.1007/s10729-018-9448-4
Teo, M., Bean N. G., & Ross J.
(2019). Optimised prophylactic vaccination in metapopulations.
bioRxiv. 559732. doi: 10.1101/559732
Bean, N. G., 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.
Crotty, S. M., Minh B. Quang, Bean N. G., Holland B. R., Tuke J., Jermiin L. S., et al.
(2019). GHOST: recovering historical signal from heterotachously-evolved sequence alignments.
bioRxiv. 174789. doi: 10.1101/174789
Glenny, V., Tuke S. Jonathan, Bean N. G., & 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.
Glonek, M., Tuke J., Mitchell L., & Bean N. G.
(2019). Semi-supervised graph labelling reveals increasing partisanship in the United States Congress.
Applied Network Science. 4(1), 62. doi: 10.1007/s41109-019-0185-5
Bean, N. G., O'Reilly M. M., & Palmowski Z.
(2019). Yaglom limit for Stochastic Fluid Models.
arXiv. arXiv:1908.10827v1 .
Glenny, V., Tuke J., Bean N. G., & Mitchell L.
(2019). A framework for streamlined statistical prediction using topic models.
arXiv. arXiv:1904.06941v1.
Bean, N. G., Latouche G., & Taylor P.
(2018). Physical Interpretations for Quasi-Birth-and-Death Process Algorithms.
Queueing Models and Service Management. 1(2), 59-78.
Rebuli, N. P., Bean N. G., & Ross J.V.
(2018). Estimating the basic reproductive number during the early stages of an emerging epidemic.
Theoretical Population Biology. 119, 26-36. doi: 10.1016/j.tpb.2017.10.004
Bean, N. G., Nguyen G. T., & Poloni F.
(2018). Doubling algorithms for stationary distributions of fluid queues: A probabilistic interpretation.
Performance Evaluation. 125, 1-20. doi: 10.1016/j.peva.2018.06.001
Fitzgerald, S., & Bean N. G.
(2018). Population correlations do not support the existence of set points for blood levels of calcium or glucose - a new model for homeostasis.
Physiological Reports. 6(1), e13551. doi: 10.14814/phy2.13551
Rohrlach, AB., Bean N. G., Glonek G., Holland B., Tobler R., Tuke J., et al.
(2018). Dimension Reduction and Visualization for Single-copy Alignments via Generalized PCA.
bioRxiv. 338442. doi: 10.1101/338442
Tuke, J., Nguyen A., Nasim M., Mellor D., Wickramasinghe A., Bean N. G., et al.
(2018). Pachinko Prediction: A Bayesian method for event prediction from social media data.
arXiv preprint arXiv:1809.08427.
Price, D. J., Bean N. G., Ross J. V., & Tuke J.
(2018). Designing group dose-response studies in the presence of transmission.
Mathematical Biosciences. 304, 62 - 78. doi: 10.1016/j.mbs.2018.07.007
Price, D. J., Bean N. G., Ross J.V., & Tuke J.
(2018). An induced natural selection heuristic for finding optimal Bayesian experimental designs.
Computational Statistics & Data Analysis. 126, 112–124. doi: 10.1016/j.csda.2018.04.011
Gower, G., Tuke S., Rohrlach A. B., Soubrier J., Llamas B., Bean N. G., et al.
(2018). Population size history from short genomic scaffolds: how short is too short?.
bioRxiv. doi: 10.1101/382036
Samuelson, A., O'Reilly MM., & Bean N. G.
(2017). On the generalized reward generator for stochastic fluid models: A new equation for Ψ .
Stochastic Models. 1 - 29. doi: 10.1080/15326349.2017.1347883
Price, D. J., Bean N. G., Ross J.V., & Tuke J.
(2017). An Induced Natural Selection Heuristic for Evaluating Optimal Bayesian Experimental Designs.
arXiv preprint arXiv:1703.05511.
Rebuli, N. P., Bean N. G., & Ross J.V.
(2017). Hybrid Markov chain models of S–I–R disease dynamics.
Journal of Mathematical Biology. 75(3), 521 - 541. doi: 10.1007/s00285-016-1085-2
Crotty, S. M., Minh B. Quang, Bean N. G., Holland B. R., Tuke J., Jermiin L. S., et al.
(2017). GHOST: Recovering Historical Signal from Heterotachously-evolved Sequence Alignments.
bioRxiv. 174789. doi: 10.1101/174789
Ballard, P. G., Bean N. G., & Ross J.V.
(2017). Intervention to maximise the probability of epidemic fade-out.
Mathematical Biosciences. 293, 1 - 10. doi: 10.1016/j.mbs.2017.08.003
Varney, J., Zarrouk S. J., Bean N. G., & Bendall B.
(2017). Performance measures in geothermal power developements.
Renewable Energy. 101, 835 - 844. doi: 10.1016/j.renene.2016.09.046
Samuelson, A., Haigh A., O'Reilly MM., & Bean N. G.
(2017). Stochastic model for maintenance in continuously deteriorating systems.
European Journal of Operational Research. 259(3), 1169 - 1179. doi: 10.1016/j.ejor.2016.11.032
Fitzgerald, S. Paul, Bean N. G., & Fitzgerald L. Nicole
(2017). Population data indicate that thyroid regulation is consistent with an equilibrium-point model, but not with a set-point model.
Temperature. 4(2), 114 - 116. doi: 10.1080/23328940.2017.1281370
Fitzgerald, S., Bean N. G., & Ruberu R.
(2017). A method of decision analysis quantifying the effects of age and comorbidities on the probability of deriving significant benefit from medical treatments.
Journal of Comorbidity. 7(1), 50 - 63. doi: 10.15256/joc.2017.7.93
Tobler, R., Rohrlach AB., Soubrier J., Bover P., Llamas B., Tuke J., et al.
(2017). Aboriginal mitogenomes reveal 50,000 years of regionalism in Australia.
Nature. 544, 180–184. doi: 10.1038/nature21416
Price, D. J., Bean N. G., Ross J.V., & Tuke J.
(2016). On the efficient determination of optimal Bayesian experimental designs using ABC: A case study in optimal observation of epidemics.
Journal of Statistical Planning and Inference. 172, 1-15. doi: 10.1016/j.jspi.2015.12.008
Dini, S., Binder B. J., Fischer S. C., Mattheyer C., Schmitz A., Stelzer E. H. K., et al.
(2016). Identifying the necrotic zone boundary in tumour spheroids with pair-correlation functions.
Journal of The Royal Society Interface. 13(123), doi: 10.1098/rsif.2016.0649
Lydeamore, M., Bean N. G., Black A. J., & Ross J.V.
(2016). Choice of antiviral allocation scheme for pandemic influenza depends on strain transmissibility, delivery delay and stockpile size.
Bulletin of Mathematical Biology. 78(2), 293-321. doi: 10.1007/s11538-016-0144-6
Ballard, P. G., Bean N. G., & Ross J.V.
(2016). The probability of epidemic fade-out is non-monotonic in transmission rate for the Markovian SIR model with demography.
Journal of Theoretical Biology. 393, 170-178. doi: 10.1016/j.jtbi.2016.01.012
Bean, N. G., Eshragh A., & Ross J.V.
(2016). Fisher Information for a partially observable simple birth process.
Communications in Statistics - Theory and Methods. 45(24), 7161-7183. doi: 10.1080/03610926.2014.978024
Fitzgerald, S. P., & Bean N. G.
(2016). The relationship between population T4/TSH set point data and T4/TSH physiology.
Journal of Thyroid Research. 1-7. doi: 10.1155/2016/6351473
Bean, N. G., Elliott R., Eshragh A., & Ross J.V.
(2015). On binomial observations of continuous-time Markovian population models.
Journal of Applied Probability. 52(02), 457-472. doi: 10.1017/S0021900200012572
Robinson, S. J., Souter N. J., Bean N. G., Ross J.V., Thompson R. M., & Bjornsson K. T.
(2015). Statistical description of wetland hydrological connectivity to the River Murray in South Australia under both natural and regulated conditions.
Journal of Hydrology. 531, 929-939. doi: 10.1016/j.jhydrol.2015.10.006
Davies, K.J., Green J.E.F., Bean N. G., Binder B.J., & Ross J.V.
(2014). On the derivation of approximations to cellular automata models and the assumption of independence.
Mathematical Biosciences. 253, 63-71. doi: 10.1016/j.mbs.2014.04.004
Feenstra, J., McGarvey R., Linnane A., Punt A. E., & Bean N. G.
(2014). Environmental influences on daily commercial catch rates of South Australia's southern rock lobster (Jasus edwardsii).
Fisheries Oceanography. 23(4), 362-374. doi: 10.1111/fog.12069
