4 citations · 5 across the 2 of their papers we have counts for
6 papers
An introduction and tutorial to model-based clustering in education via Gaussian mixture modelling
Luca Scrucca, Mohammed Saqr, Sonsoles López-Pernas +1
Heterogeneity has been a hot topic in recent educational literature. Several calls have been voiced to adopt methods that capture different patterns or subgroups within students be…
GP-BART: a novel Bayesian additive regression trees approach using Gaussian processes
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 performanc…
Accounting for shared covariates in semi-parametric Bayesian additive regression trees
Estevão B. Prado, Andrew C. Parnell, Keefe Murphy +3
We propose some extensions to semi-parametric models based on Bayesian additive regression trees (BART). In the semi-parametric BART paradigm, the response variable is approximated…
Clustering Longitudinal Life-Course Sequences Using Mixtures of Exponential-Distance Models
Keefe Murphy, Thomas Brendan Murphy, Raffaella Piccarreta +1
Sequence analysis is an increasingly popular approach for analysing life courses represented by ordered collections of activities experienced by subjects over time. Here, we analys…
Gaussian Parsimonious Clustering Models with Covariates and a Noise Component
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 proposi…
Infinite Mixtures of Infinite Factor Analysers
Keefe Murphy, Cinzia Viroli, Isobel Claire Gormley
Factor-analytic Gaussian mixture models are often employed as a model-based approach to clustering high-dimensional data. Typically, the numbers of clusters and latent factors must…