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Found 5 papers, 5 papers with code
GP-BART: a novel Bayesian additive regression trees approach using Gaussian processes
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.
Accounting for shared covariates in semi-parametric Bayesian additive regression trees
We propose some extensions to semi-parametric models based on Bayesian additive regression trees (BART).
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
Infinite Mixtures of Infinite Factor Analysers
Factor-analytic Gaussian mixture models are often employed as a model-based approach to clustering high-dimensional data.
Methodology
