Is infinity that far? A Bayesian nonparametric perspective of finite mixture models Page 1 The
Annals of Statistics 2022, Vol. 50, No. 5, 2641–2663 https://doi.org/10.1214/22-AOS2201 © …

Bayesian cluster analysis offers substantial benefits over algorithmic approaches by
providing not only point estimates but also uncertainty in the clustering structure and …

In model-based clustering mixture models are used to group data points into clusters. A
useful concept introduced for Gaussian mixtures by Malsiner Walli et al.(Stat Comput 26 …

This chapter presents some of the Bayesian solutions to the different interpretations of
picking the “right” number of components in a mixture, before concluding on the ill-posed …

Within a Bayesian framework, a comprehensive investigation of mixtures of finite mixtures
(MFMs), ie, finite mixtures with a prior on the number of components, is performed. This …

Scientists and engineers are often interested in learning the number of subpopulations (or
components) present in a data set. A common suggestion is to use a finite mixture model …

The individual results of SARS-CoV-2 serological tests measured after the first pandemic
wave of 2020 cannot be directly interpreted as a probability of having been infected. Plus …

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 …

This chapter introduces the model-based clustering is related to standard heuristic clustering
methods and an overview of different ways to specify the cluster model. It provides the …

Bayesian clustering typically relies on mixture models, with each component interpreted as a
different cluster. After defining a prior for the component parameters and weights, Markov …