In Press

Shooter R, Ross E, Tawn J, Jonathan P (2019). On spatial conditional extremes for ocean storm severity. Environmetrics e2562.

Baker J., Fearnhead, P., Fox E. B. and Nemeth C. (2018). sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo. Journal of Statistical Software (to appear).

Baker, J., Fearnhead, P., Fox E. B. and Nemeth C. (2018). Control Variates for Stochastic Gradient MCMC. Statistics and Computing (to appear). doi: 10.1007/s11222-018-9826-2

Bardwell, L., Eckley, I, Fearnhead, P., Smith, S. and Spott, M. (2018). Most recent changepoint detection in panel data. M. Technometrics (to appear)

Edwards, J. A. and Leslie, D. S. (2018). Selecting Multiple Web Adverts - a Contextual Multi-armed Bandit with State Uncertainty. Journal of the Operational Research Society (in press).

Fearnhead, P., Maidstone, R., Letchford, A. (2018). Detecting Changes in Slope with an L0 Penalty. In: Journal of Computational and Graphical Statistics.

Rhodes-Leader, L., Onggo, B. S. S., Worthington, D. J. & Nelson, B. L. (2018) Multi-fidelity Simulation Optimisation For Airline Disruption Management. 2018 Winter Simulation Conference 2179–2190. Piscataway, New Jersey:  IEEE

Taylor, S.A.C., Park, T. and Eckley, I.A. (2018). Multivariate Locally Stationary Wavelet Analysis with the mvLSW R Package. Journal of Statistical Software (to appear).



A.N. Letchford & G. Souli (2019) New valid inequalities for the fixed-charge and single-node flow polytopes. Operations Research Letters, 47(5), 353-357.

A.N. Letchford & G. Souli (2019) On lifted cover inequalities: a new lifting procedure with unusual properties. Operations Research Letters, vol. 47, issue 2, pp. 83-87.



Baker, J., Fearnhead, P., Fox, E.B. and Nemeth, C. (2018). Large-scale stochastic sampling from the probability simplex. Advances in Neural Information Processing Systems 31.

Barlow, A. M., Rohrbeck, C., Sharkey, P., Shooter, R. and Simpson, E. S. (2018). A Bayesian spatio-temporal model for precipitation extremes—STOR team contribution to the EVA2017 challenge. Extremes, 21(3), 431-439.

Barnett, H. Y., Geys, H., Jacobs, T., & Jaki, T. F. (2018). Optimal Designs for Non-Compartmental Analysis of Pharmacokinetic Studies. Statistics in Biopharmaceutical Research. DOI: 10.1080/19466315.2018.1458647

Edwards, J. A. & Leslie, D. S. (2018). Diversity as a Response to User Preference Uncertainty, World Scientific, chapter 4, pp. 55-68.

Fairbrother, J., Letchford, A. N., & Briggs, K. (2018). A two-level graph partitioning problem arising in mobile wireless communications. Computational Optimization and Applications, 69(3), 653-676. DOI: 10.1007/s10589-017-9967-9

Fairbrother, J., Turner, A., & Wallace, S. (2018). Scenario generation for single-period portfolio selection problems with tail risk measures: coping with high dimensions and integer variables. INFORMS Journal on Computing, 30(3), 472-491. DOI: 10.1287/ijoc.2017.0790

Fearnhead, P., Maidstone, R., & Letchford, A. (2018). Detecting changes in slope with an L0 penalty. Journal of Computational and Graphical Statistics. DOI: 10.1080/10618600.2018.1512868

Hofmeyr, D., Pavlidis, N., Eckley, I., (2018). Minimum spectral connectivity projection pursuit. Statistics and Computing.

Ludkin, M., Eckley, I. and Neal, P. (2018). Dynamic stochastic block models: Parameter estimation and detection of changes in community structure. Statistics and Computing, 28, 1201-1213.

Moss, H., Leslie, D. S., & Rayson, P. E. (2018). Using J-K-fold Cross Validation to Reduce Variance When Tuning NLP Models. In Proceedings of COLING 2018 (Proceedings of COLING 2018).

Park, T., Eckley, I. A., and Ombao, H. (2018) .Dynamic classification of multivariate time series using the multivariate locally stationary wavelet model. Signal Processing, 152, 118-129.

Peng, H., Pavlidis, N. G., Eckley, I. A., Tsalamanis, I. (2019). Subspace Clustering of Very Sparse High-Dimensional Data. 2018 IEEE Conference on Big Data. IEEE.

Pike-Burke, C., Agrawal, S., Szepesvari, C., Grunewalder, S. (2018). Bandits with Delayed, Aggregated Anonymous Feedback. Proceedings of the International Conference on Machine Learning, 10-15 July 2018, Stockholmsmassan, Stockholm Sweden. Dy, J (ed.). PMLR, p 4105-4123 19 p. (Proceedings of Machine Learning Research; vol. 80).

Rhodes-Leader, L., Onggo, B. S. S., Worthington, D. J. & Nelson, B. L. (2018) “Airline Disruption Recovery Using Symbiotic Simulation and Multi-fidelity Modelling.” Proceedings of the 9th Operational Research Society Simulation Workshop (SW18). Operational Research Society, p. 146-155 10 p.

Rohrbeck, C., D A Costain, A Frigessi; Bayesian spatial monotonic multiple regression, Biometrika, Volume 105, Issue 3, 1 September 2018, Pages 691–707,

Rohrbeck, C., Eastoe, E. F., Frigessi, A., and Tawn, J. A.(2018). Extreme Value Modelling of Water-related Insurance Claims. Annals of Applied Statistics, 12(1) 246-282.

Stubington, E., Ehrgott, M., Glyn, S., & Nohadani, O. (2018). Evaluating the Quality of Radiotherapy Treatment Plans for Prostate Cancer. In S. Huber, M. J. Geiger, & A. T. de Almeida (Eds.), Cases based on Multiple Criteria Decision Making/Aiding methods: Building and Solving Decision Models with Computer Implementations Springer, 26 p.

Tawn, J. A., Shooter, R., Towe, R. and Lamb, R. (2018). Modelling Spatial Extreme Events with Environmental Applications. Spatial Statistics.

Wilson, R., Eckley, I.A., Nunes, M.A. and Park, T. (2018). Dynamic detection of anomalous regions within distributed acoustic sensing data streams using locally stationary time series. Accepted for publication in Data Mining and Knowledge Discovery.



Bardwell, L. and Fearnhead, P. (2017). Bayesian detection of abnormal segments in multiple time series. Bayesian Analysis, 12, 193-218.

Barnett, H., Geys, H., Jacobs, T and Jaki, T. (2017). Multiple Comparisons of Model Averaged Derived Parameters with Applications to the Comparison of Sampling Methods in Pharmacokinetic Studies. Statistics in Medicine, 36 (27), 4301-4315.

Edwards, J, Fearnhead, P. and Glazebrook, K. (2017).‘On the Identification and Mitigation of Weaknesses in the Knowledge Gradient Policy for Multi-Armed Bandits’, Probability in the Engineering and Informational Sciences, doi: 10.1017/S0269964816000279.

Fairbrother, J. and Letchford, A.N. (2017). Projection results for the k-partition problem. Discrete Optimization, 26, p. 97-111.

Hofmeyr, D. (2017). Clustering by minimum cut hyperplanes. IEEE Transactions on Pattern Analysis and Machine Intelligence, 39(8), 1547-1560.

Jackson, H., Barnett, L., Jordan, K., Dziedzic, K., Cottrell, E., Finney, A., Paskins, Z., Edwards, J. (2017) Patterns of routine primary care for osteoarthritis in the UK: a cross-sectional electronic health records study. BMJ Open

Maidstone, R., Hocking, T., Rigaill, G., Fearnhead, P. (2017). On Optimal Multiple Changepoint Algorithms for Large Data. Statistics and Computing, 27, 519-533.

Morgan, L., Nelson, B.L., Titman, A.C. and Worthington, D.J. (2017). Detecting Bias due to Input Modelling in Computer Simulation. In: WSC' 17 Proceedings of the 2017 Winter Simulation Conference. Piscataway, NJ, USA : IEEE Press p.1974-1985 p.13.   

Onggo, B.S.S. and Morgan, L. (2017). The Importance of Input Uncertainty Quantification. In Social Science Simulation.

Pike-Burke, C., Grünewälder, S. (2017)."Optimistic Planning for the Stochastic Knapsack Problem," in Proceedings of 20th International Conference on Artificial Intelligence and Statistics

Sharkey, P. and Tawn, J.A. (2017). A Poisson process reparameterisation for Bayesian inference for extremes. Extremes, 20, 239-263.

Taylor, S.L., Eckley, I.A. and Nunes, M.A. (2017). Multivariate locally stationary 2D wavelet processes with application to colour texture analysis. Statistics and Computing 27 (4), 1129-1143.

Winter, H. C., Tawn, J. A. (2017). kth-order Markov models for assessing heatwave risks. Extremes, 20, 393-415.

Winter, H. C., Tawn, J. A. and Brown, S. J. (2017). Detecting changing behaviour of heatwaves with climate change. Dynamics and Statistics of the Climate System, 1, doi: 10.1093/climsys/dzw006



Crespo Del Granado P., Pang Z. and Wallace S. W. (2016). Synergy of Smart grids and hybrid distributed generation on the value of energy storage, Applied Energy, forthcoming APEN_7540.

Hofmeyr, D. (2016). On the topology of genetic algorithms. Proc. IJCAI (2016). 

Hofmeyr, D., Pavlidis, N., Eckley, I.,(2016). Divisive clustering of high dimensional data streams. Statistics and Computing 26(5), 1101-1120.

Kereszturi, M., Tawn, J. A. (2016). Properties of extremal dependence models built on bivariate max-linearity. Journal of Multivariate Analysis, 155, 52-71.

Morgan, L. E., Nelson, B. L., Worthington, D. and Titman, A. (2016).“Input Uncertainty Quantification for Simulation Models with Piecewise-Constant Non-Stationary Poisson Arrival Processes” The Winter Simulation Conference.

Nemeth, C., Fearnhead, P. and Mihaylova, L. (2016). Particle approximations of the score and observed information matrix for parameter estimation in state space models with linear computational cost. Journal of Computational and Graphical Statistics. 25, 1138-1157.

Nemeth, C.,Sherlock, C. and Fearnhead, P. (2016). Particle Metropolis-adjusted Langevin algorithms. Biometrika. 103, 701-717.

Pavlidis, N., Hofmeyr, D., Tasoulis, S., (2016). Minimum density hyperplanes. Journal of Machine Learning Research, 17(156):1-33.

Randell, D., Turnbull, K., Ewans, K., and Jonathan, P. (2016). Bayesian inference for non-stationary marginal extremes. Environmetrics; 27: 439–450.

Suen, D., Worthington, D. and Allen, M. (2016), Using infinite-server queues to underpin model-based performance indicators: at IMA  Health & Social Care Conference 2016 and at European Conference on Queueing Theory 2016.

Williamson, F., Jacko, P., Jaki, T. and Villar S. (2016). A Bayesian adaptive design for clinical trials in rare diseases. Computational Statistics & Data Analysis, 113, 136-153.

Winter, H. C. and Tawn, J. A. (2016). Modelling heatwaves in central France: a case study in extremal dependence. Journal of the Royal Statistical Society: Series C, 65(3), 345-365.

Winter, H. C., Tawn, J. A. and Brown, S. J. (2016). Modelling the effect of the El Niño-Southern Oscillation on extreme spatial temperature events over Australia. Annals of Applied Statistics, 10(4), 2075-2101.

Yates, K. and Pavlidis, N. (2016).Minimum density hyperplanes in the feature space. Advances in High Dimensional Big Data" workshop, IEEE international conference on Big Data, Washington.



Crespo Del Granado P., Wallace S. W. and Pang Z. (2015). The impact of wind uncertainty on the strategic valuation of distributed electricity storage, Computational Management Science, Vol 13, 1, pp 5-27, DOI: 10.1007/s10287-015-0235-0.

Haynes, K., Eckley, I. A., and Fearnhead, P., (2015). Computationally efficient changepoint detection for a range of penalties. Journal of Computational and Graphical Statistics, 26 (1), 134-143, 2017.

Haynes, K., Fearnhead, P., and Eckley, I.A. (2015). A computationally efficient nonparametric approach for changepoint detection. Statistics and Computing 27 (5), 1293-1305, 2017.

Hofmeyr, D. And Pavlidis, N. (2015). Maximum Clusterability Divisive Clustering. Proc. IEEE SSCI (CIDM)

Hofmeyr, D. And Pavlidis, N. (2015). Semi-supervised Spectral Connectivity Projection Pursuit. Proc. PRASA RobMech Int. Conf.

Kereszturi, M., Tawn, J. A. and Jonathan, P. (2015). Assessing extremal dependence of North Sea storm severity. Ocean Engineering, 118, 242-259.

Letchford, A.N. and Nasiri, S.D. (2015). The Steiner travelling salesman problem with correlated costs. Eur. J. Oper. Res., 245(1), 62–69.



Briggs, K., & Fairbrother, J. (2014). Enhanced algorithms for TV whitespace power maximization. In Antennas and Propagation (EuCAP), 2014 8th European Conference on IEEE. DOI: 10.1109/EuCAP.2014.6902623

Crespo Del Granado P., Wallace S. W. and Pang Z. (2014). The value of electricity storage in domestic homes: A smart grid perspective. Energy Systems, Vol 5, 2, pp 211-232,DOI: 10.1007/s12667-013-0108.

James, T., Glazebrook, K. D., Lin, K. (2014). Developing Effective Service Policies for Multiclass Queues with Abandonment: Asymptotic Optimality and Approximate Policy Improvement. INFORMS Journal of Computing, 28, 2, p. 251-264.

Park, T. Eckley, I. A. and Ombao H. C. (2014). Estimating time-evolving dependence quantities between signals via multivariate locally stationary wavelet processes”, IEEE Transactions on Signal Processing, 62, 5240-5250.

Randell, D., Zanini, E., Vogel, M., Ewans, K., Jonathan, P. (2014).“Omnidirectional Return values for Storm Severity from Directional Extreme Values Models: The Effect of Physical Environment and Sample Size. Proceedings of 33nd International Conference on Ocean, Offshore and Arctic Engineering, San Francisco OMAE2014-23156.



Davies, R., Mihaylova, L., Pavlidis, N. and Eckley, I.A. (2013). The effect of recovery algorithms on compressive sensing background subtraction” In Sensor Data Fusion: Trends, Solutions, Applications (SDF), 2013 Workshop on (pp. 1-6). IEEE.

Letchford, A. N. & Nasiri, S. D. (2013). Compact formulations of the Steiner TSP and related problems. Eur. J. Oper. Res., 228, 83-92.

Letchford, A.N. and Nasiri, S.D. (2013). Pricing Routines for Vehicle Routing with Time Windows on Road Networks. Comput. & Oper. Res., 51, 331-337.

Maidstone, R., Pickering, B. (2013). Comment on "Multiscale Change-Point Inference" by K. Frick, A. Munk and H. Sieling. Journal of the Royal Statistical Society, Series B.

Nemeth, C., Fearnhead, P. and Mihaylova, L. (2013). Sequential Monte Carlo Methods for State and Parameter Estimation in Abruptly Changing Environments. IEEE Transactions on Signal Processing, Vol. 62, No. 5, pp. 1245-1255.



Nemeth, C., Fearnhead, P., Mihaylova, L. and Vorley, D. (2012).“Bearings-Only Tracking with Particle Filtering for Joint Parameter and State Estimation,” 15Th International Conference on Information Fusion, pp. 824-831.

Nemeth, C., Fearnhead, P., Mihaylova, L. and Vorley, D. (2012).“Particle learning for state and parameter estimation,” 9Th IET Data Fusion and Target Tracking Conference (DF&TT 2012), London U.K.

Reeve, D.T., Randall, D., Ewans, K.C. and Jonathan, P. (2012). Uncertainty due to choice of measurement scale in Extreme Value modelling of North Sea Storm Severity. Ocean Engineering, 53, 164-176.