14 citations · 17 across the 5 of their papers we have counts for
5 papers
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…
Dynamic Mixture of Finite Mixtures of Factor Analysers with Automatic Inference on the Number of Clusters and Factors
Margarita Grushanina, Sylvia Frühwirth-Schnatter
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 clust…
Generalized Cumulative Shrinkage Process Priors with Applications to Sparse Bayesian Factor Analysis
Sylvia Frühwirth-Schnatter
The paper discusses shrinkage priors which impose increasing shrinkage in a sequence of parameters. We review the cumulative shrinkage process (CUSP) prior of Legramanti et al. (20…
Sparse Bayesian factor analysis when the number of factors is unknown
Sylvia Frühwirth-Schnatter, Darjus Hosszejni, Hedibert Freitas Lopes
There has been increased research interest in the subfield of sparse Bayesian factor analysis with shrinkage priors, which achieve additional sparsity beyond the natural parsimonit…
Sparse Bayesian State-Space and Time-Varying Parameter Models
Sylvia Frühwirth-Schnatter, Peter Knaus
In this chapter, we review variance selection for time-varying parameter (TVP) models for univariate and multivariate time series within a Bayesian framework. We show how both cont…