Computational aspects of fitting mixture models via the expectation-maximization algorithm. - Adrian O'Hagan; Thomas Brendan Murphy; Isobel Claire Gormley (Comput. Stat. Data Anal., 2012)

Clustering with the multivariate normal inverse Gaussian distribution. - Adrian O'Hagan; Thomas Brendan Murphy; Isobel Claire Gormley; Paul D. McNicholas; Dimitris Karlis (Comput. Stat. Data Anal., 2016)

Investigation of parameter uncertainty in clustering using a Gaussian mixture model via jackknife, bootstrap and weighted likelihood bootstrap. - Adrian O'Hagan; Thomas Brendan Murphy; Luca Scrucca; Isobel Claire Gormley (Comput. Stat., 2019)

Improved model-based clustering performance using Bayesian initialization averaging. - Adrian O'Hagan; Arthur J. White (Comput. Stat., 2019)

AI-based modeling and data-driven evaluation for smart manufacturing processes. - Mohammadhossein Ghahramani; Yan Qiao; MengChu Zhou; Adrian O'Hagan; James Sweeney (IEEE CAA J. Autom. Sinica, 2020)

Intelligent Geodemographic Clustering Based on Neural Network and Particle Swarm Optimization. - Mohammadhossein Ghahramani; Adrian O'Hagan; Mengchu Zhou; James Sweeney (IEEE Trans. Syst. Man Cybern. Syst., 2022)

Variational Bayes latent class analysis for EHR-based phenotyping with large real-world data. - Brian Buckley; Adrian O'Hagan; Marie C. Galligan (Frontiers Appl. Math. Stat., 2024)

Joint modelling of the body and tail of bivariate data. - Lídia Maria André; Jennifer L. Wadsworth; Adrian O'Hagan (Comput. Stat. Data Anal., 2024)

A Dirichlet stochastic block model for composition-weighted networks. - Iuliia Promskaia; Adrian O'Hagan; Michael Fop (Comput. Stat. Data Anal., 2025)