Select Publications
Preprints
2024, Calibrated Multivariate Regression with Localized PIT Mappings
,2024, Analysing symbolic data by pseudo-marginal methods, http://arxiv.org/abs/2408.04419v1
,2024, Flexible max-stable processes for fast and efficient inference, http://arxiv.org/abs/2407.13958v4
,2024, Parameter estimation of Gompertz model for tumorgrowth: which likelihood to choose?, http://dx.doi.org/10.21203/rs.3.rs-3999289/v1
,2024, Model-Free Local Recalibration of Neural Networks, http://dx.doi.org/10.48550/arxiv.2403.05756
,2022, A correlated pseudo-marginal approach to doubly intractable problems, http://arxiv.org/abs/2210.02734v1
,2022, Modularized Bayesian analyses and cutting feedback in likelihood-free inference, http://dx.doi.org/10.48550/arxiv.2203.09782
,2021, An Introduction to Quantum Computing for Statisticians and Data Scientists, http://dx.doi.org/10.48550/arxiv.2112.06587
,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
,2021, Modelling age-related changes in executive functions of soccer players, http://arxiv.org/abs/2105.01226v1
,2020, Hidden Group Time Profiles: Heterogeneous Drawdown Behaviours in Retirement, http://arxiv.org/abs/2009.01505v2
,2020, Likelihood-based inference for modelling packet transit from thinned flow summaries, http://dx.doi.org/10.48550/arxiv.2008.13424
,2020, Stressor equivalents: A framework to prevent perverse outcomes in data-poor systems, http://dx.doi.org/10.22541/au.159283264.49749008
,2019, Logistic regression models for aggregated data, http://dx.doi.org/10.48550/arxiv.1912.03805
,2019, Multiclass classification of growth curves using random change points and heterogeneous random effects, http://arxiv.org/abs/1909.07550v1
,2019, Efficient Bayesian synthetic likelihood with whitening transformations, http://dx.doi.org/10.48550/arxiv.1909.04857
,2019, Composite likelihood methods for histogram-valued random variables, http://dx.doi.org/10.48550/arxiv.1908.11548
,2019, High-dimensional inference using the extremal skew-$t$ process, http://dx.doi.org/10.48550/arxiv.1907.10187
,2019, Likelihood-free approximate Gibbs sampling, http://dx.doi.org/10.48550/arxiv.1906.04347
,2019, Estimation and uncertainty quantification for extreme quantile regions, http://dx.doi.org/10.48550/arxiv.1904.08251
,2019, Vector operations for accelerating expensive Bayesian computations -- a tutorial guide, http://dx.doi.org/10.48550/arxiv.1902.09046
,2018, Extremal properties of the multivariate extended skew-normal distribution, http://dx.doi.org/10.48550/arxiv.1810.00680
,2018, Variance reduction properties of the reparameterization trick, http://arxiv.org/abs/1809.10330v3
,2018, New models for symbolic data analysis, http://dx.doi.org/10.48550/arxiv.1809.03659
,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
,2018, Extremal properties of the univariate extended skew-normal distribution, http://dx.doi.org/10.48550/arxiv.1805.03316
,2018, High-dimensional ABC, http://dx.doi.org/10.48550/arxiv.1802.09725
,2017, Estimating global species richness using symbolic data meta-analysis, http://dx.doi.org/10.48550/arxiv.1711.03202
,2017, Recalibration: A post-processing method for approximate Bayesian computation, http://dx.doi.org/10.48550/arxiv.1704.06374
,2016, Variational Bayes with Synthetic Likelihood, http://dx.doi.org/10.48550/arxiv.1608.03069
,2016, Constructing Likelihood Functions for Interval-valued Random Variables, http://dx.doi.org/10.48550/arxiv.1608.00107
,2016, Exploratory data analysis for moderate extreme values using non-parametric kernel methods, http://dx.doi.org/10.48550/arxiv.1602.08807
,2015, Models for extremal dependence derived from skew-symmetric families, http://dx.doi.org/10.48550/arxiv.1507.00108
,2015, Extending approximate Bayesian computation methods to high dimensions via a Gaussian copula model, http://dx.doi.org/10.48550/arxiv.1504.04093
,2014, Functional regression approximate Bayesian computation for Gaussian process density estimation, http://dx.doi.org/10.48550/arxiv.1410.8276
,2013, Diagnostic tools of approximate Bayesian computation using the coverage property, http://dx.doi.org/10.48550/arxiv.1301.3166
,2012, Approximate Bayesian Computation via Regression Density Estimation, http://dx.doi.org/10.48550/arxiv.1212.1479
,2012, Simultaneous adjustment of bias and coverage probabilities for confidence intervals, http://dx.doi.org/10.48550/arxiv.1210.3405
,2012, A Comparative Review of Dimension Reduction Methods in Approximate Bayesian Computation, http://dx.doi.org/10.48550/arxiv.1202.3819
,2011, Approximate Bayesian computation and Bayes linear analysis: Towards high-dimensional ABC, http://dx.doi.org/10.48550/arxiv.1112.4755
,2010, Bayesian Symbol Detection in Wireless Relay Networks via Likelihood-Free Inference, http://dx.doi.org/10.48550/arxiv.1007.4603
,2010, Adaptive Optimal Scaling of Metropolis-Hastings Algorithms Using the Robbins-Monro Process, http://dx.doi.org/10.48550/arxiv.1006.3690
,2010, A note on target distribution ambiguity of likelihood-free samplers, http://dx.doi.org/10.48550/arxiv.1005.5201
,2009, On Bayesian Curve Fitting Via Auxiliary Variables, http://dx.doi.org/10.48550/arxiv.0911.1894
,2009, Likelihood-based inference for max-stable processes, http://dx.doi.org/10.48550/arxiv.0902.3060
,Bayesian Inference, Monte Carlo Sampling and Operational Risk., http://dx.doi.org/10.2139/ssrn.2980407
,Dynamic Quantile Function Models, http://dx.doi.org/10.2139/ssrn.2999451
,Likelihood-Free Bayesian Inference for -Stable Models, http://dx.doi.org/10.2139/ssrn.2980440
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