By Professor Scott Anthony Sisson

Preprints

Ramírez-Torres EE; Castañeda ARS; Rández L; Sisson SA; Cabrales LEB; Montijano JI, 2024, Parameter estimation of Gompertz model for tumorgrowth: which likelihood to choose?, , http://dx.doi.org/10.21203/rs.3.rs-3999289/v1

Torres R; Nott DJ; Sisson SA; Rodrigues T; Reis JG; Rodrigues GS, 2024, Model-Free Local Recalibration of Neural Networks,

Yang Y; Quiroz M; Kohn R; Sisson SA, 2022, A correlated pseudo-marginal approach to doubly intractable problems, , http://arxiv.org/abs/2210.02734v1

Chakraborty A; Nott DJ; Drovandi C; Frazier DT; Sisson SA, 2022, Modularized Bayesian analyses and cutting feedback in likelihood-free inference, , http://dx.doi.org/10.48550/arxiv.2203.09782

Lopatnikova A; Tran M-N; Sisson SA, 2021, An Introduction to Quantum Computing for Statisticians and Data Scientists, , http://dx.doi.org/10.48550/arxiv.2112.06587

Ramirez-Torres EE; Selva Castañeda AR; Randez L; Valdés García LE; Cabrales LEB; Sisson SA; Montijano JI, 2021, A new model of unreported COVID-19 cases outperforms three known epidemic-growth models in describing data from Cuba and Spain, , http://dx.doi.org/10.1101/2021.06.29.21259707

Chin V; Beavan A; Fransen J; Mayer J; Kohn R; Ryan LM; Sisson SA, 2021, Modelling age-related changes in executive functions of soccer players, , http://arxiv.org/abs/2105.01226v1

Balnozan I; Fiebig DG; Asher A; Kohn R; Sisson SA, 2020, Hidden Group Time Profiles: Heterogeneous Drawdown Behaviours in Retirement, , http://arxiv.org/abs/2009.01505v2

Rahman PA; Beranger B; Roughan M; Sisson SA, 2020, Likelihood-based inference for modelling packet transit from thinned flow summaries, , http://dx.doi.org/10.48550/arxiv.2008.13424

Plein M; Brien KO; Holden M; Adams M; Baker C; Bean N; Sisson S; Bode M; Mengersen K; Madden EM, 2020, Stressor equivalents: A framework to prevent perverse outcomes in data-poor systems, , http://dx.doi.org/10.22541/au.159283264.49749008

Whitaker T; Beranger B; Sisson SA, 2019, Logistic regression models for aggregated data, , http://dx.doi.org/10.48550/arxiv.1912.03805

Chin V; Lee JYL; Ryan LM; Kohn R; Sisson SA, 2019, Multiclass classification of growth curves using random change points and heterogeneous random effects, , http://arxiv.org/abs/1909.07550v1

Priddle JW; Sisson SA; Frazier DT; Drovandi C, 2019, Efficient Bayesian synthetic likelihood with whitening transformations, , http://dx.doi.org/10.48550/arxiv.1909.04857

Whitaker T; Beranger B; Sisson SA, 2019, Composite likelihood methods for histogram-valued random variables, , http://dx.doi.org/10.48550/arxiv.1908.11548

Beranger B; Stephenson AG; Sisson SA, 2019, High-dimensional inference using the extremal skew-$t$ process, , http://dx.doi.org/10.48550/arxiv.1907.10187

Rodrigues GS; Nott DJ; Sisson SA, 2019, Likelihood-free approximate Gibbs sampling, , http://dx.doi.org/10.48550/arxiv.1906.04347

Beranger B; Padoan SA; Sisson SA, 2019, Estimation and uncertainty quantification for extreme quantile regions, , http://dx.doi.org/10.48550/arxiv.1904.08251

Warne DJ; Sisson SA; Drovandi C, 2019, Vector operations for accelerating expensive Bayesian computations -- a tutorial guide, , http://dx.doi.org/10.48550/arxiv.1902.09046

Beranger B; Padoan SA; Xu Y; Sisson SA, 2018, Extremal properties of the multivariate extended skew-normal distribution, , http://dx.doi.org/10.48550/arxiv.1810.00680

Xu M; Quiroz M; Kohn R; Sisson SA, 2018, Variance reduction properties of the reparameterization trick, , http://arxiv.org/abs/1809.10330v3

Beranger B; Lin H; Sisson SA, 2018, New models for symbolic data analysis, , http://dx.doi.org/10.48550/arxiv.1809.03659

Chin V; Gunawan D; Fiebig DG; Kohn R; Sisson SA, 2018, Efficient data augmentation for multivariate probit models with panel data: An application to general practitioner decision-making about contraceptives, , http://dx.doi.org/10.1111/rssc.12393

Beranger B; Padoan SA; Xu Y; Sisson SA, 2018, Extremal properties of the univariate extended skew-normal distribution, , http://dx.doi.org/10.48550/arxiv.1805.03316

Nott DJ; Ong VM-H; Fan Y; Sisson SA, 2018, High-dimensional ABC, , http://dx.doi.org/10.48550/arxiv.1802.09725

Lin H; Caley MJ; Sisson SA, 2017, Estimating global species richness using symbolic data meta-analysis, , http://dx.doi.org/10.48550/arxiv.1711.03202

Rodrigues GS; Prangle D; Sisson SA, 2017, Recalibration: A post-processing method for approximate Bayesian computation, , http://dx.doi.org/10.48550/arxiv.1704.06374

Ong VM-H; Nott DJ; Tran M-N; Sisson SA; Drovandi CC, 2016, Variational Bayes with Synthetic Likelihood, , http://dx.doi.org/10.48550/arxiv.1608.03069

Zhang X; Beranger B; Sisson SA, 2016, Constructing Likelihood Functions for Interval-valued Random Variables, , http://dx.doi.org/10.48550/arxiv.1608.00107

Beranger B; Duong T; Perkins-Kirkpatrick SE; Sisson SA, 2016, Exploratory data analysis for moderate extreme values using non-parametric kernel methods, , http://dx.doi.org/10.48550/arxiv.1602.08807

Beranger B; Padoan SA; Sisson SA, 2015, Models for extremal dependence derived from skew-symmetric families, , http://dx.doi.org/10.48550/arxiv.1507.00108

Li J; Nott DJ; Fan Y; Sisson SA, 2015, Extending approximate Bayesian computation methods to high dimensions via a Gaussian copula model, , http://dx.doi.org/10.48550/arxiv.1504.04093

Rodrigues GS; Nott DJ; Sisson SA, 2014, Functional regression approximate Bayesian computation for Gaussian process density estimation, , http://dx.doi.org/10.48550/arxiv.1410.8276

Prangle D; Blum MGB; Popovic G; Sisson SA, 2013, Diagnostic tools of approximate Bayesian computation using the coverage property, , http://dx.doi.org/10.48550/arxiv.1301.3166

Fan Y; Nott DJ; Sisson SA, 2012, Approximate Bayesian Computation via Regression Density Estimation, , http://dx.doi.org/10.48550/arxiv.1212.1479

Menendez P; Fan Y; Garthwaite PH; Sisson SA, 2012, Simultaneous adjustment of bias and coverage probabilities for confidence intervals, , http://dx.doi.org/10.48550/arxiv.1210.3405

Blum MGB; Nunes MA; Prangle D; Sisson SA, 2012, A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation, , http://dx.doi.org/10.48550/arxiv.1202.3819

Nott DJ; Fan Y; Marshall L; Sisson SA, 2011, Approximate Bayesian computation and Bayes linear analysis: Towards high-dimensional ABC, , http://dx.doi.org/10.48550/arxiv.1112.4755

Peters GW; Nevat I; Sisson SA; Fan Y; Yuan J, 2010, Bayesian Symbol Detection in Wireless Relay Networks via Likelihood-Free Inference, , http://dx.doi.org/10.48550/arxiv.1007.4603

Garthwaite PH; Fan Y; Sisson SA, 2010, Adaptive Optimal Scaling of Metropolis-Hastings Algorithms Using the Robbins-Monro Process, , http://dx.doi.org/10.48550/arxiv.1006.3690

Sisson SA; Peters GW; Briers M; Fan Y, 2010, A note on target distribution ambiguity of likelihood-free samplers, , http://dx.doi.org/10.48550/arxiv.1005.5201

Fan Y; Dortet-Bernadet J-L; Sisson SA, 2009, On Bayesian Curve Fitting Via Auxiliary Variables, , http://dx.doi.org/10.48550/arxiv.0911.1894

Padoan SA; Ribatet M; Sisson SA, 2009, Likelihood-based inference for max-stable processes, , http://dx.doi.org/10.48550/arxiv.0902.3060

Peters GW; Sisson S, Bayesian Inference, Monte Carlo Sampling and Operational Risk., , http://dx.doi.org/10.2139/ssrn.2980407

Chen WY; Peters GW; Gerlach RH; Sisson S, Dynamic Quantile Function Models, , http://dx.doi.org/10.2139/ssrn.2999451

Peters G; Sisson S; Fan Y, Likelihood-Free Bayesian Inference for -Stable Models, , http://dx.doi.org/10.2139/ssrn.2980440

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