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Found 6 papers, 2 papers with code
Unobserved classes and extra variables in high-dimensional discriminant analysis
In supervised classification problems, the test set may contain data points belonging to classes not observed in the learning phase.
Parsimonious Bayesian Factor Analysis for modelling latent structures in spectroscopy data
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
Gaussian Mixture models (GMMs) are a powerful tool for clustering, classification and density estimation when clustering structures are embedded in the data.
Clustering Longitudinal Life-Course Sequences Using Mixtures of Exponential-Distance Models
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
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
Bayesian nonparametric Plackett-Luce models for the analysis of preferences for college degree programmes
In this paper we propose a Bayesian nonparametric model for clustering partial ranking data.
