Search Results for author: Thomas Brendan Murphy

Found 6 papers, 2 papers with code

Unobserved classes and extra variables in high-dimensional discriminant analysis

no code implementations3 Feb 2021 Michael Fop, Pierre-Alexandre Mattei, Charles Bouveyron, Thomas Brendan Murphy

In supervised classification problems, the test set may contain data points belonging to classes not observed in the learning phase.

Classification General Classification +2

Parsimonious Bayesian Factor Analysis for modelling latent structures in spectroscopy data

no code implementations29 Jan 2021 Alessandro Casa, Tom F. O'Callaghan, Thomas Brendan Murphy

Hence statistical tools studying potential differences among milk samples coming from animals on different feeding systems are required, thus providing increased security around the authenticity of the products.

Methodology Applications

Handling missing data in model-based clustering

no code implementations4 Jun 2020 Alessio Serafini, Thomas Brendan Murphy, Luca Scrucca

Gaussian Mixture models (GMMs) are a powerful tool for clustering, classification and density estimation when clustering structures are embedded in the data.

Clustering Data Augmentation +3

Clustering Longitudinal Life-Course Sequences Using Mixtures of Exponential-Distance Models

1 code implementation21 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

Gaussian Parsimonious Clustering Models with Covariates and a Noise Component

2 code implementations15 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

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