1 code implementation • 5 Apr 2022 • Mateus Maia, Keefe Murphy, Andrew C. Parnell
The Bayesian additive regression trees (BART) model is an ensemble method extensively and successfully used in regression tasks due to its consistently strong predictive performance and its ability to quantify uncertainty.
2 code implementations • 17 Aug 2021 • Estevão B. Prado, Andrew C. Parnell, Keefe Murphy, Nathan McJames, Ann O'Shea, Rafael A. Moral
We propose some extensions to semi-parametric models based on Bayesian additive regression trees (BART).
1 code implementation • 21 Aug 2019 • Keefe Murphy, Thomas Brendan Murphy, Raffaella Piccarreta, Isobel Claire Gormley
Here, we analyse a survey data set containing information on the career trajectories of a cohort of Northern Irish youths tracked between the ages of 16 and 22.
Methodology Applications
2 code implementations • 15 Nov 2017 • Keefe Murphy, Thomas Brendan Murphy
We consider model-based clustering methods for continuous, correlated data that account for external information available in the presence of mixed-type fixed covariates by proposing the MoEClust suite of models.
Methodology
1 code implementation • 24 Jan 2017 • Keefe Murphy, Isobel Claire Gormley, Cinzia Viroli
Factor-analytic Gaussian mixture models are often employed as a model-based approach to clustering high-dimensional data.
Methodology