176 citations · 304 across the 10 of their papers we have counts for
12 papers
CliPS -- How to identify cluster distributions in Bayesian mixture models
Gertraud Malsiner-Walli, Sylvia Frühwirth-Schnatter, Bettina Grün
We propose the CliPS procedure when fitting Bayesian mixture models in the context of model-based clustering to identify the cluster distributions while simultaneously assessing th…
Bayesian Finite Mixture Models
Bettina Grün, Gertraud Malsiner-Walli
Finite mixture models are a useful statistical model class for clustering and density approximation. In the Bayesian framework finite mixture models require the specification of su…
Without Pain -- Clustering Categorical Data Using a Bayesian Mixture of Finite Mixtures of Latent Class Analysis Models
Gertraud Malsiner-Walli, Bettina Grün, Sylvia Frühwirth-Schnatter
We propose a Bayesian approach for model-based clustering of multivariate categorical data where variables are allowed to be associated within clusters and the number of clusters i…
How many data clusters are in the Galaxy data set? Bayesian cluster analysis in action
Bettina Grün, Gertraud Malsiner-Walli, Sylvia Frühwirth-Schnatter
In model-based clustering, the Galaxy data set is often used as a benchmark data set to study the performance of different modeling approaches. Aitkin (2001) compares maximum likel…
Spying on the prior of the number of data clusters and the partition distribution in Bayesian cluster analysis
Jan Greve, Bettina Grün, Gertraud Malsiner-Walli +1
Cluster analysis aims at partitioning data into groups or clusters. In applications, it is common to deal with problems where the number of clusters is unknown. Bayesian mixture mo…
Generalized mixtures of finite mixtures and telescoping sampling
Sylvia Frühwirth-Schnatter, Gertraud Malsiner-Walli, Bettina Grün
Within a Bayesian framework, a comprehensive investigation of mixtures of finite mixtures (MFMs), i.e., finite mixtures with a prior on the number of components, is performed. This…
Comparing Spike and Slab Priors for Bayesian Variable Selection
Gertraud Malsiner-Walli, Helga Wagner
An important task in building regression models is to decide which regressors should be included in the final model. In a Bayesian approach, variable selection can be performed usi…
Computational Solutions for Bayesian Inference in Mixture Models
Gilles Celeux, Kaniav Kamary, Gertraud Malsiner-Walli +2
This chapter surveys the most standard Monte Carlo methods available for simulating from a posterior distribution associated with a mixture and conducts some experiments about the…
From here to infinity - sparse finite versus Dirichlet process mixtures in model-based clustering
Sylvia Frühwirth-Schnatter, Gertraud Malsiner-Walli
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 (2016) are sparse fi…
Effect fusion using model-based clustering
Gertraud Malsiner-Walli, Daniela Pauger, Helga Wagner
In social and economic studies many of the collected variables are measured on a nominal scale, often with a large number of categories. The definition of categories is usually not…
Model-based clustering based on sparse finite Gaussian mixtures
Gertraud Malsiner-Walli, Sylvia Frühwirth-Schnatter, Bettina Grün
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 an…
Identifying Mixtures of Mixtures Using Bayesian Estimation
Gertraud Malsiner-Walli, Sylvia Frühwirth-Schnatter, Bettina Grün
The use of a finite mixture of normal distributions in model-based clustering allows to capture non-Gaussian data clusters. However, identifying the clusters from the normal compon…