Model-based clustering
IC Gormley, TB Murphy… - Annual Review of Statistics …, 2023 - annualreviews.org
Clustering is the task of automatically gathering observations into homogeneous groups,
where the number of groups is unknown. Through its basis in a statistical modeling …
where the number of groups is unknown. Through its basis in a statistical modeling …
Adaptability and stability of Coffea canephora to dynamic environments using the Bayesian approach
FL Partelli, FA da Silva, AM Covre, G Oliosi… - Scientific Reports, 2022 - nature.com
The objective of this work was to use the Bayesian approach, modeling the interaction of
coffee genotypes with the environment, using a bisegmented regression to identify stable …
coffee genotypes with the environment, using a bisegmented regression to identify stable …
Infinite mixtures of infinite factor analysers
Infinite Mixtures of Infinite Factor Analysers Page 1 Bayesian Analysis (2020) 15, Number 3,
pp. 937–963 Infinite Mixtures of Infinite Factor Analysers Keefe Murphy ∗ , Cinzia Viroli † …
pp. 937–963 Infinite Mixtures of Infinite Factor Analysers Keefe Murphy ∗ , Cinzia Viroli † …
On the identifiability of Bayesian factor analytic models
P Papastamoulis, I Ntzoufras - Statistics and Computing, 2022 - Springer
A well known identifiability issue in factor analytic models is the invariance with respect to
orthogonal transformations. This problem burdens the inference under a Bayesian setup …
orthogonal transformations. This problem burdens the inference under a Bayesian setup …
Model-based clustering of censored data via mixtures of factor analyzers
Mixtures of factor analyzers (MFA) provide a promising tool for modeling and clustering high-
dimensional data that contain an overwhelmingly large number of attributes measured on …
dimensional data that contain an overwhelmingly large number of attributes measured on …
Dynamic mixture of finite mixtures of factor analysers with automatic inference on the number of clusters and factors
M Grushanina, S Frühwirth-Schnatter - arXiv preprint arXiv:2307.07045, 2023 - arxiv.org
Mixtures of factor analysers (MFA) models represent a popular tool for finding structure in
data, particularly high-dimensional data. While in most applications the number of clusters …
data, particularly high-dimensional data. While in most applications the number of clusters …
Clustering multivariate data using factor analytic Bayesian mixtures with an unknown number of components
P Papastamoulis - Statistics and Computing, 2020 - Springer
Recent work on overfitting Bayesian mixtures of distributions offers a powerful framework for
clustering multivariate data using a latent Gaussian model which resembles the factor …
clustering multivariate data using a latent Gaussian model which resembles the factor …
Model based clustering of multinomial count data
P Papastamoulis - Advances in Data Analysis and Classification, 2023 - Springer
We consider the problem of inferring an unknown number of clusters in multinomial count
data, by estimating finite mixtures of multinomial distributions with or without covariates. Both …
data, by estimating finite mixtures of multinomial distributions with or without covariates. Both …
On Bayesian analysis of parsimonious Gaussian mixture models
Cluster analysis is the task of grouping a set of objects in such a way that objects in the
same cluster are similar to each other. It is widely used in many fields including machine …
same cluster are similar to each other. It is widely used in many fields including machine …
[PDF][PDF] Parallel tempering and dimension reduction schemes for Bayesian estimation of multivariate mixture models with unknown number of components
P Papastamoulis, GR Milos - aueb.gr
Parallel tempering and dimension reduction schemes for Bayesian estimation of multivariate
mixture models with unknown number of Page 1 Parallel tempering and dimension reduction …
mixture models with unknown number of Page 1 Parallel tempering and dimension reduction …