Discovering transcriptional modules by Bayesian data integration. - Richard S. Savage; Zoubin Ghahramani; Jim E. Griffin; Bernard J. de la Cruz; David L. Wild (Bioinform., 2010)

Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes. - Jim E. Griffin; M. F. J. Steel (Comput. Stat. Data Anal., 2010)

Modeling overdispersion with the normalized tempered stable distribution. - M. Kolossiatis; Jim E. Griffin; M. F. J. Steel (Comput. Stat. Data Anal., 2011)

Slice sampling mixture models. - Maria Kalli; Jim E. Griffin; Stephen G. Walker (Stat. Comput., 2011)

Bayesian correlated clustering to integrate multiple datasets. - Paul D. W. Kirk; Jim E. Griffin; Richard S. Savage; Zoubin Ghahramani; David L. Wild (Bioinform., 2012)

Cross-validation prior choice in Bayesian probit regression with many covariates. - Demetris Lamnisos; Jim E. Griffin; M. F. J. Steel (Stat. Comput., 2012)

A Bayesian semiparametric model for volatility with a leverage effect. - E.-I. Delatola; Jim E. Griffin (Comput. Stat. Data Anal., 2013)

On adaptive Metropolis-Hastings methods. - Jim E. Griffin; Stephen G. Walker (Stat. Comput., 2013)

On Bayesian nonparametric modelling of two correlated distributions. - M. Kolossiatis; Jim E. Griffin; M. F. J. Steel (Stat. Comput., 2013)

An adaptive truncation method for inference in Bayesian nonparametric models. - Jim E. Griffin (Stat. Comput., 2016)

Sequential Monte Carlo methods for mixtures with normalized random measures with independent increments priors. - Jim E. Griffin (Stat. Comput., 2017)

Discussion of "Nonparametric Bayesian Inference in Applications": Bayesian nonparametric methods in econometrics. - Jim E. Griffin; Maria Kalli; Mark F. J. Steel (Stat. Methods Appl., 2018)

ParticleMDI: particle Monte Carlo methods for the cluster analysis of multiple datasets with applications to cancer subtype identification. - Nathan Cunningham; Jim E. Griffin; David L. Wild (Adv. Data Anal. Classif., 2020)

Bayesian Models Applied to Cyber Security Anomaly Detection Problems. - José A. Perusquía; Jim E. Griffin; Cristiano Villa (CoRR, 2020)

On a Bayesian Approach to Malware Detection and Classification through n-gram Profiles. - José A. Perusquía; Jim E. Griffin; Cristiano Villa (CoRR, 2020)

An adaptive MCMC method for Bayesian variable selection in logistic and accelerated failure time regression models. - Kitty Yuen Yi Wan; Jim E. Griffin (Stat. Comput., 2021)

Adaptive random neighbourhood informed Markov chain Monte Carlo for high-dimensional Bayesian variable selection. - Xitong Liang; Samuel Livingstone; Jim E. Griffin (Stat. Comput., 2022)

Structure Learning with Adaptive Random Neighborhood Informed MCMC. - Xitong Liang; Alberto Caron; Samuel Livingstone; Jim E. Griffin (NeurIPS, 2023)

Adaptive MCMC for Bayesian Variable Selection in Generalised Linear Models and Survival Models. - Xitong Liang; Samuel Livingstone; Jim E. Griffin (Entropy, 2023)

Uncovering Student Engagement Patterns in Moodle with Interpretable Machine Learning. - Laura J. Johnston; Jim E. Griffin; Ioanna Manolopoulou; Takoua Jendoubi (CoRR, 2024)

Beta-CoRM: A Bayesian approach for n-gram profiles analysis. - José A. Perusquía; Jim E. Griffin; Cristiano Villa (Comput. Stat. Data Anal., 2025)

A real-time metric of online engagement monitoring. - Laura J. Johnston; Jim E. Griffin; Ioanna Manolopoulou; Takoua Jendoubi (CoRR, 2025)

Fast Bayesian inference in a class of sparse linear mixed effects models. - Maria-Zafeiria Spyropoulou; James G. Hopker; Jim E. Griffin (Stat. Comput., 2025)