Select Publications

Book Chapters

Li Y; Fan X; Chen L; Li B; Sisson SA, 2021, 'Decoupling Sparsity and Smoothness in Dirichlet Belief Networks', in Machine Learning and Knowledge Discovery in Databases. Research Track, pp. 148 - 163, http://dx.doi.org/10.1007/978-3-030-86520-7_10

Nott DJ; Ong VM-H; Fan Y; Sisson SA, 2018, 'High-Dimensional ABC', in Handbook of Approximate Bayesian Computation, Chapman and Hall/CRC, pp. 211 - 241, http://dx.doi.org/10.1201/9781315117195-8

Sisson SA; Fan Y; Beaumont MA, 2018, 'Overview of ABC', in Handbook of Approximate Bayesian Computation, Chapman and Hall/CRC, pp. 3 - 54, http://dx.doi.org/10.1201/9781315117195-1

Sisson S; Fan Y, 2018, 'ABC Samplers', in Sisson S; Fan Y; Beaumont M (ed.), Handbook of Approximate Bayesian Computation, Chapman & Hall, pp. 87 - 123, https://www.amazon.com/Handbook-Approximate-Computation-Handbooks-Statistical/dp/1439881502

Nott D; Ong V; Fan Y; Sisson S, 2018, 'High-dimensional approximate Bayesian computation', in Sisson S; Fan Y; Beaumont M (ed.), Handbook of Approximate Bayesian Computation, Chapman & Hall, pp. 211 - 242

Rodrigues G; Francis A; Sisson S; Tanaka M, 2018, 'Inferences on the acquisition of multidrug resistance in Mycobacterium tuberculosis using molecular epidemiological data', in Sisson S; Fan Y; Beaumont M (ed.), Approximate Bayesian Computation Likelihood-Free Methods for Complex Models, Chapman & Hall

Sisson S; Fan Y; Beaumont M, 2018, 'Overview of Approximate Bayesian Computation', in Sisson S; Fan Y; Beaumont M (ed.), Handbook of Approximate Bayesian Computation, Chapman & Hall, pp. 3 - 54, https://www.amazon.com/Handbook-Approximate-Computation-Handbooks-Statistical/dp/1439881502

Erhardt R; sisson SA, 2016, 'Modelling extremes using approximate Bayesian computation', in Dey D; Yan J (ed.), Extreme Value Modeling and Risk Analysis Methods and Applications, CRC Press, pp. 281 - 306

Sisson SA; Fan Y, 2011, 'Likelihood-free MCMC', in Brooks S; Gelman A; Jones GL; Meng X-L (ed.), Handbook of Markov Chain Monte Carlo, Taylor and Francis Group, USA, pp. 313 - 333

Fan Y; Sisson S, 2011, 'Reversible Jump MCMC', in Brooks S; Gelman A; Jones GL; Meng X-L (ed.), Handbook of Markov Chain Monte Carlo, edn. Chapman & Hall-CRC Handbooks of Modern Statistical Methods, Taylor and Francis Group, USA, pp. 67 - 87, http://gateway.webofknowledge.com/gateway/Gateway.cgi?GWVersion=2&SrcApp=PARTNER_APP&SrcAuth=LinksAMR&KeyUT=WOS:000294600700004&DestLinkType=FullRecord&DestApp=ALL_WOS&UsrCustomerID=891bb5ab6ba270e68a

Edited Books

Sisson S; Fan Y; Beaumont M, (eds.), 2018, Handbook of Approximate Bayesian Computation, Chapman and Hall/CRC Press, https://www.crcpress.com/Handbook-of-Approximate-Bayesian-Computation/Sisson-Fan-Beaumont/p/book/9781439881507

Journal articles

Beranger B; Lin H; Sisson S, 2023, 'New models for symbolic data analysis', Advances in Data Analysis and Classification, 17, pp. 659 - 699, http://dx.doi.org/10.1007/s11634-022-00520-8

Fan X; Li Y; Chen L; Li B; Sisson SA, 2023, 'Hawkes Processes With Stochastic Exogenous Effects for Continuous-Time Interaction Modelling', IEEE Transactions on Pattern Analysis and Machine Intelligence, 45, pp. 1848 - 1861, http://dx.doi.org/10.1109/TPAMI.2022.3161649

Chakraborty A; Nott DJ; Drovandi CC; Frazier DT; Sisson SA, 2023, 'Modularized Bayesian analyses and cutting feedback in likelihood-free inference', Statistics and Computing, 33, http://dx.doi.org/10.1007/s11222-023-10207-5

Shahriari S; Sisson SA; Rashidi T, 2023, 'Copula ARMA-GARCH modelling of spatially and temporally correlated time series data for transportation planning use', Transportation Research Part C: Emerging Technologies, 146, http://dx.doi.org/10.1016/j.trc.2022.103969

Plein M; O'Brien KR; Holden MH; Adams MP; Baker CM; Bean NG; Sisson SA; Bode M; Mengersen KL; McDonald-Madden E, 2022, 'Modeling total predation to avoid perverse outcomes from cat control in a data-poor island ecosystem', Conservation Biology, 36, http://dx.doi.org/10.1111/cobi.13916

Man N; Sisson SA; McKetin R; Chrzanowska A; Bruno R; Dietze PM; Price O; Degenhardt L; Gibbs D; Salom C; Peacock A, 2022, 'Trends in methamphetamine use, markets and harms in Australia, 2003–2019', Drug and Alcohol Review, 41, pp. 1041 - 1052, http://dx.doi.org/10.1111/dar.13468

Warne DJ; Sisson SA; Drovandi C, 2022, 'Vector Operations for Accelerating Expensive Bayesian Computations – A Tutorial Guide', Bayesian Analysis, 17, pp. 593 - 622, http://dx.doi.org/10.1214/21-BA1265

Lin H; Caley MJ; Sisson SA, 2022, 'Estimating global species richness using symbolic data meta-analysis', Ecography, 2022, http://dx.doi.org/10.1111/ecog.05617

Chen WY; Peters GW; Gerlach RH; Sisson SA, 2022, 'Dynamic quantile function models', Quantitative Finance, 22, pp. 1665 - 1691, http://dx.doi.org/10.1080/14697688.2022.2053193

Priddle JW; Sisson SA; Frazier DT; Turner I; Drovandi C, 2022, 'Efficient Bayesian Synthetic Likelihood With Whitening Transformations', Journal of Computational and Graphical Statistics, 31, pp. 50 - 63, http://dx.doi.org/10.1080/10618600.2021.1979012

Rahman P; Beranger B; Sisson S; Roughan M, 2022, 'Likelihood-Based Inference for Modelling Packet Transit From Thinned Flow Summaries', IEEE Transactions on Signal and Information Processing over Networks, 8, pp. 571 - 583, http://dx.doi.org/10.1109/TSIPN.2022.3188457

Li Y; Fan X; Chen L; Li B; Sisson SA, 2022, 'Smoothing graphons for modelling exchangeable relational data', Machine Learning, 111, pp. 319 - 344, http://dx.doi.org/10.1007/s10994-021-06046-y

Beranger B; Stephenson AG; Sisson SA, 2021, 'High-dimensional inference using the extremal skew-t process', Extremes, 24, pp. 653 - 685, http://dx.doi.org/10.1007/s10687-020-00376-1

Man N; Chrzanowska A; Price O; Bruno R; Dietze PM; Sisson SA; Degenhardt L; Salom C; Morris L; Farrell M; Peacock A, 2021, 'Trends in cocaine use, markets and harms in Australia, 2003–2019', Drug and Alcohol Review, 40, pp. 946 - 956, http://dx.doi.org/10.1111/dar.13252

Beranger B; Padoan SA; Sisson SA, 2021, 'Correction to: Estimation and uncertainty quantification for extreme quantile regions (Extremes, (2021), 24, 2, (349-375), 10.1007/s10687-019-00364-0)', Extremes, 24, pp. 377 - 378, http://dx.doi.org/10.1007/s10687-021-00408-4

Beranger B; Padoan SA; Sisson SA, 2021, 'Estimation and uncertainty quantification for extreme quantile regions', Extremes, 24, pp. 349 - 375, http://dx.doi.org/10.1007/s10687-019-00364-0

Whitaker T; Beranger B; Sisson SA, 2021, 'Logistic Regression Models for Aggregated Data', Journal of Computational and Graphical Statistics, 30, pp. 1049 - 1067, http://dx.doi.org/10.1080/10618600.2021.1895816

Whitaker T; Beranger B; Sisson SA, 2020, 'Composite likelihood methods for histogram-valued random variables', Statistics and Computing, 30, pp. 1459 - 1477, http://dx.doi.org/10.1007/s11222-020-09955-5

Clark S; Sisson SA; Sharma A, 2020, 'Tools for enhancing the application of self-organizing maps in water resources research and engineering', Advances in Water Resources, 143, http://dx.doi.org/10.1016/j.advwatres.2020.103676

Adams MP; Koh EJY; Vilas MP; Collier CJ; Lambert VM; Sisson SA; Quiroz M; McDonald-Madden E; McKenzie LJ; O'Brien KR, 2020, 'Predicting seagrass decline due to cumulative stressors', Environmental Modelling and Software, 130, http://dx.doi.org/10.1016/j.envsoft.2020.104717

Rodrigues GS; Nott DJ; Sisson SA, 2020, 'Likelihood-free approximate Gibbs sampling', Statistics and Computing, 30, pp. 1057 - 1073, http://dx.doi.org/10.1007/s11222-020-09933-x

Chin V; Gunawan D; Fiebig DG; Kohn R; Sisson SA, 2020, 'Efficient data augmentation for multivariate probit models with panel data: an application to general practitioner decision making about contraceptives', Journal of the Royal Statistical Society. Series C: Applied Statistics, 69, pp. 277 - 300, http://dx.doi.org/10.1111/rssc.12393

Adams MP; Sisson SA; Helmstedt KJ; Baker CM; Holden MH; Plein M; Holloway J; Mengersen KL; McDonald-Madden E, 2020, 'Informing management decisions for ecological networks, using dynamic models calibrated to noisy time-series data', Ecology Letters, 23, pp. 607 - 619, http://dx.doi.org/10.1111/ele.13465

Zhang X; Beranger B; Sisson SA, 2020, 'Constructing likelihood functions for interval-valued random variables', Scandinavian Journal of Statistics, 47, pp. 1 - 35, http://dx.doi.org/10.1111/sjos.12395

Li C; Xie HB; Mengersen K; Fan X; Da Xu RY; Sisson SA; Van Huffel S, 2020, 'Bayesian nonnegative matrix factorization with dirichlet process mixtures', IEEE Transactions on Signal Processing, 68, pp. 3860 - 3870, http://dx.doi.org/10.1109/TSP.2020.3003120

Shahriari S; Ghasri M; Sisson SA; Rashidi T, 2020, 'Ensemble of ARIMA: combining parametric and bootstrapping technique for traffic flow prediction', Transportmetrica A: Transport Science, 16, pp. 1552 - 1573, http://dx.doi.org/10.1080/23249935.2020.1764662

Li C; Xie HB; Fan X; Xu RYD; Van Huffel S; Sisson SA; Mengersen K, 2019, 'Image denoising based on nonlocal Bayesian singular value thresholding and Stein's unbiased risk estimator', IEEE Transactions on Image Processing, 28, pp. 4899 - 4911, http://dx.doi.org/10.1109/TIP.2019.2912292

Herger N; Abramowitz G; Sherwood S; Knutti R; Angélil O; Sisson SA, 2019, 'Ensemble optimisation, multiple constraints and overconfidence: a case study with future Australian precipitation change', Climate Dynamics, 53, pp. 1581 - 1596, http://dx.doi.org/10.1007/s00382-019-04690-8

Bengtson SA; Meissner KJ; Menviel L; Sisson SA; Wilkin J, 2019, 'Evaluating the Extent of North Atlantic Deep Water and the Mean Atlantic δ13C From Statistical Reconstructions', Paleoceanography and Paleoclimatology, 34, pp. 1022 - 1036, http://dx.doi.org/10.1029/2019PA003589

Beranger B; Padoan SA; Xu Y; Sisson SA, 2019, 'Extremal properties of the multivariate extended skew-normal distribution, Part B', Statistics and Probability Letters, 147, pp. 105 - 114, http://dx.doi.org/10.1016/j.spl.2018.11.031

Beranger B; Padoan SA; Xu Y; Sisson SA, 2019, 'Extremal properties of the univariate extended skew-normal distribution, Part A', Statistics and Probability Letters, 147, pp. 73 - 82, http://dx.doi.org/10.1016/j.spl.2018.09.018

Béranger B; Duong T; Perkins-Kirkpatrick SE; Sisson SA, 2019, 'Tail density estimation for exploratory data analysis using kernel methods', Journal of Nonparametric Statistics, 31, pp. 144 - 174, http://dx.doi.org/10.1080/10485252.2018.1537442

Ong VMH; Nott DJ; Tran MN; Sisson SA; Drovandi CC, 2018, 'Likelihood-free inference in high dimensions with synthetic likelihood', Computational Statistics and Data Analysis, 128, pp. 271 - 291, http://dx.doi.org/10.1016/j.csda.2018.07.008

Rodrigues GS; Prangle D; Sisson SA, 2018, 'Recalibration: A post-processing method for approximate Bayesian computation', Computational Statistics and Data Analysis, 126, pp. 53 - 66, http://dx.doi.org/10.1016/j.csda.2018.04.004

Ong VMH; Nott DJ; Tran MN; Sisson SA; Drovandi CC, 2018, 'Variational Bayes with synthetic likelihood', Statistics and Computing, 28, pp. 971 - 988, http://dx.doi.org/10.1007/s11222-017-9773-3

Clark S; Sharma A; Sisson SA, 2018, 'Patterns and comparisons of human-induced changes in river flood impacts in cities', Hydrology and Earth System Sciences, 22, pp. 1793 - 1810, http://dx.doi.org/10.5194/hess-22-1793-2018

Gross MH; Donat MG; Alexander LV; Sisson SA, 2018, 'The sensitivity of daily temperature variability and extremes to dataset choice', Journal of Climate, 31, pp. 1337 - 1359, http://dx.doi.org/10.1175/JCLI-D-17-0243.1

Clark S; Sharma A; Sisson SA, 2017, 'Patterns and comparisons of human-induced changes on river flood impacts in cities', , http://dx.doi.org/10.5194/hess-2017-162

Beranger B; Padoan SA; Sisson SA, 2017, 'Models for Extremal Dependence Derived from Skew-symmetric Families', Scandinavian Journal of Statistics, 44, pp. 21 - 45, http://dx.doi.org/10.1111/sjos.12240


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