Modelling based on finite mixture distributions is a rapidly developing area with the range of
applications exploding. Finite mixture models are nowadays applied in such diverse areas …

ABSTRACT A natural Bayesian approach for mixture models with an unknown number of
components is to take the usual finite mixture model with symmetric Dirichlet weights, and …

In the past ten years there has been a dramatic increase of interest in the Bayesian analysis
of finite mixture models. This is primarily because of the emergence of Markov chain Monte …

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 © …

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 …

The major implementational problem for reversible jump Markov chain Monte Carlo methods
is that there is commonly no natural way to choose jump proposals since there is no …

This paper presents a robust mixture modeling framework using the multivariate skew t
distributions, an extension of the multivariate Student'st family with additional shape …

Skew-normal and skew-t distributions have proved to be useful for capturing skewness and
kurtosis in data directly without transformation. Recently, finite mixtures of such distributions …

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 …