In the framework of Bayesian model-based clustering based on a finite mixture of Gaussian
distributions, we present a joint approach to estimate the number of mixture components and …

K∑ k= 1 ηkfT (y| θk),(10.1) where y is the realisation of a univariate or multivariate, discrete-
or continuousvalued random variable and the component densities fT (y| θk) arise from the …

Label switching is a well-known and fundamental problem in Bayesian estimation of mixture
or hidden Markov models. In case that the prior distribution of the model parameters is the …

Label switching is a well-known problem in the Bayesian analysis of mixture models. On the
one hand, it complicates inference, and on the other hand, it has been perceived as a …

The purpose of this study is to provide guidance on a process for including latent class
predictors in regression mixture models. We first examine the performance of current …

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 …

Online reviews provide rich information on customer satisfaction, displaying various numeric
ratings as well as detailed explanations presented in written form. However, analyzing such …

Clustering longitudinal features is a common goal in medical studies to identify distinct
disease developmental trajectories. Compared to clustering a single longitudinal feature …

During the past two decades, methods for identifying groups with different trends in
longitudinal data have become of increasing interest across many areas of research. To …

The traditional estimation of mixture regression models is based on the assumption of
normality (symmetry) of component errors and thus is sensitive to outliers, heavy-tailed …