Books and Book Chapters

Goan, E., & Fookes C. (2020).  Bayesian Neural Networks: An Introduction and Survey. ase Studies in Applied Bayesian Data Science: CIRM Jean-Morlet Chair, Fall 2018. 1, 45-87. doi: 10.1007/978-3-030-42553-1_3
Bian, L., Cui T., Sofronov G., & Keith J. (2020).  Network Structure Change Point Detection by Posterior Predictive Discrepancy. (Tuffin, B., & L'Ecuyer P., Ed.).Springer Proceedings in Mathematics & Statistics: Monte Carlo and Quasi-Monte Carlo MethodsNetwork Structure Change Point Detection by Posterior Predictive Discrepancy. 324, 107 - 123. doi: 10.1007/978-3-030-43465-6_5
MacNamara, S., McLean W., & Burrage K. (2019).  Wider contours and adaptive contours. (Wood, D., De Gier J., Praeger C. E., & Tao T., Ed.).2017 MATRIX Annals. 2, 79-98. doi: 10.1007/978-3-030-04161-8_7
Ye, N., Roosta-Khorasani F., Cui T., De Gier J., Praeger C. E., & Tao T. (2019).  Optimization Methods for Inverse Problems. (Wood, D., Ed.).2017 MATRIX Annals. doi: 10.1007/978-3-030-04161-8_9
Błaszczyszyn, B., Haenggi M., Keeler H., & Mukherjee S. (2018).  Stochastic Geometry Analysis of Cellular Networks. doi: 10.1017/9781316677339
[Anonymous] (2018).  2016 MATRIX Annals. (De Gier, J., Praeger C. E., Wood D. R., & Tao T., Ed.).MATRIX Book Series. 1, 656. doi: 10.1007/978-3-319-72299-3
Drovandi, C. C. (2018).  ABC and Indirect Inference. Handbook of Approximate Bayesian Computation. 179-210.
Drovandi, C. C., Grazian C., Mengersen KL., & Robert C. (2018).  Approximating the Likelihood in ABC. Handbook of Approximate Bayesian Computation. 321-368.
Fan, Y., Meikle S.. R., Angelis G.., & Sitek A.. (2018).  ABC in Nuclear Imaging. Handbook of Approximate Bayesian Computation. 623-648.
Gertsbakh, I. B., Shpungin Y., & Vaisman R. (2018).  Reliability of a Network with Heterogeneous Components. (Lisnianski, A., Frenkel I., & Karagrigoriou A., Ed.).Recent Advances in Multi-state Systems Reliability. 3 - 18. doi: 10.1007/978-3-319-63423-4_1
Nott, D. J., Ong V.. M. - H., Fan Y., & Sisson S. (2018).  High-Dimensional ABC. Handbook of Approximate Bayesian Computation. 211-242. doi: 10.1201/9781315117195-8
Paam, P.., Berretta R.., & Heydar M. (2018).  An Integrated Loss-Based Optimization Model for Apple Supply Chain. (N., K., J. E., & R. B., Ed.).Operations Research Proceedings 2017. 663 - 669. doi: 10.1007/978-3-319-89920-6_88
Rodrigues, G. S., Francis A. R., Sisson S., & Tanaka M. M. (2018).  Inferences on the Acquisition of Multi-Drug Resistance in Mycobacterium Tuberculosis Using Molecular Epidemiological Data. Handbook of Approximate Bayesian Computation. 481-511.
[Anonymous] (2018).  Handbook of Approximate Bayesian Computation. (Sisson, S., Fan Y., & Beaumont M. A., Ed.). doi: 10.1201/9781315117195
Sisson, S., Fan Y., & beaumont M.. A. (2018).  Overview of ABC. Handbook of Approximate Bayesian Computation. 3-54.
Sisson, S., & Fan Y. (2018).  ABC Samplers. Handbook of Approximate Bayesian Computation. 87-124.
Asanjarani, A., & Nazarathy Y. (2016).  A Queueing Approximation of MMPP/PH/1. Queueing Theory and Network Applications. 383, 41-51. doi: 10.1007/978-3-319-22267-7
Erhardt, R., & Sisson S. (2016).  Modelling extremes using approximate Bayesian computation. (Dey, D., & Yan J., Ed.).Extreme Value Modelling and Risk Analysis. 281-306.
Grolemund, G., & Wickham H. (2016).  R for Data Science. Import, tidy, transform, visualize, and model data.
Hall, P. (2016).  Contributions of Rabi Bhattacharya to the Central Limit Theory and Normal Approximation. . (Denker, M., & Waymire E. C., Ed.).Rabi N. Bhattacharya: Selected Papers. 3-13. doi: 10.1007/978-3-319-30190-7_1
Moore, D. F. (2016).  Applied Survival Analysis Using R.
Rubinstein, R. Y., & Kroese D. (2016).  Simulation and the Monte Carlo Methods. 1-432.
Steponavič\.e, I., Shirazi-Manesh M., Hyndman R. J., Smith-Miles K., Villanova L., Pardalos P.. M., et al. (2016).  On sampling methods for costly multi-objective black-box optimization. Advances in Stochastic and Deterministic Global Optimization. 273–296.
Geweke, J., Durham G., & Xu H. (2015).  Bayesian Inference for Logistic Regression Models Using Sequential Posterior Simulation . (Upadhyay, S.K., Singh U., Dey D.K., & Loganathan A., Ed.).Current Trends in Bayesian Methodology with Applications. 289-312.
[Anonymous] (2015).  Biosecurity Surveillance: Quantitative Approaches. (Jarrad, F., Low-Choy S., & Mengersen KL., Ed.). doi: 10.1079/9781780643595.0000
Johnson, S., Mengersen KL., Ormsby M., & Whittle P. (2015).  Using Bayesian networks to model surveillance in complex plant and animal health systems. (Jarrad, F., Low-Choy S., & Mengersen KL., Ed.).Biosecurity Surveillance: Quantitative Approaches. 278-295. doi: 10.1079/9781780643595.0278
Kroese, D., & Botev Z.I. (2015).  Spatial Process Simulation. (Schmidt, V., Ed.).Stochastic Geometry, Spatial Statistics and Random Fields. 369-404. doi: 10.1007/978-3-319-10064-7_12
Mengersen, KL., McGree J., & Schmid C. H. (2015).  Statistical Analysis of N-of-1 Trials. (Nikles, J., & Mitchell G., Ed.).The Essential Guide to N-of-1 Trials in Health. 135-153. doi: 10.1007/978-94-017-7200-6_12
Mengersen, KL., McGree J., & Schmid C. H. (2015).  Systematic Review and Meta-analysis Using N-of-1 Trials. (Nikles, J., & Mitchell G., Ed.).The Essential Guide to N-of-1 Trials in Health. 211-231. doi: 10.1007/978-94-017-7200-6_16
Mittnty, M., Whittle P., Burgman P., & Mengersen KL. (2015).  The role of surveillance in evaluating and comparing international quarantine systems. (Jarrad, F., Low-Choy S., & Mengersen KL., Ed.).Biosecurity Surveillance: Quantitative Approaches. 137-150. doi: 10.1079/9781780643595.0137
Murray, J., Whittle P., Jarrad F., Barrett S., Stoklosa R., & Mengersen KL. (2015).  Design of a surveillance system for non-indigenous species on Barrow Island: plants case study. (Nikles, J., & Mitchell G., Ed.).Biosecurity surveillance: quantitative approaches. 203-216. doi: 10.1079/9781780643595.0203
Quinlan, M., Stanaway M., & Mengersen KL. (2015).  Biosecurity surveillance in agriculture and environment: a review. (Jarrad, F.., Low-Choy S.., & Mengersen K.., Ed.).Biosecurity surveillance: quantitative approaches. 9-42. doi: 10.1079/9781780643595.0009
Rubenstein, J.H., Rubinstein B.I.P., & Bartlett P.L. (2015).  Bounding Embeddings of VC Classes into Maximum Classes. (Vovk, V., Papadopoulos H., & Gammerman A., Ed.).Measures of Complexity. 303-325. doi: 10.1007/978-3-319-21852-6_21
Sun, C., Bednarz T., Pham T.D., Vallotton P., & Wang D. (2015).  Distributed Collaborative Immersive Virtual Reality Framework for the Mining Industry. (Billingsley, J., & Brett P., Ed.).Machine Vision and Mechatronics in Practice. 39-48. doi: 10.1007/978-3-662-45514-2_4
[Anonymous] (2015).  Signal and Image Analysis for Biomedical and Life Sciences. (Sun, C., Bednarz T., Pham T.D., Vallotton P., & Wang D., Ed.).Advances in Experimental Medicine and Biology. 823, XVI, 276. doi: 10.1007/978-3-319-10984-8
van Havre, Z., & Whittle P. (2015).  Designing surveillance for emergency response. (Jarrad, F., Low-Choy S., & Mengersen KL., Ed.).Biosecurity surveillance: quantitative approaches. 123-133. doi: 10.1079/9781780643595.0123
Bednarz, T., Wang D., Arzhaeva Y., Lagerstrom R., Vallotton P., Burdett N., et al. (2014).  Cloud Based Toolbox for Image Analysis, Processing and Reconstruction Tasks. (Sun, C., Bednarz T., Pham T. D., Vallotton P., & Wang D., Ed.).Signal and Image Analysis for Biomedical and Life Sciences. 823, 191 - 205. doi: 10.1007/978-3-319-10984-8_11