A data-driven selection of the number of clusters in the Dirichlet allocation model via Bayesian mixture modelling

DOI10.1080/00949655.2019.1643345OpenAlexW2963977790WikidataQ127494257 ScholiaQ127494257MaRDI QIDQ5107497

Erlandson F. Saraiva, Adriano K. Suzuki, Carlos Alberto de Bragança Pereira

Publication date: 27 April 2020

Published in: Journal of Statistical Computation and Simulation (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/00949655.2019.1643345

zbMATH Keywords

Kullback-Leibler divergenceGibbs samplingmixture modelMetropolis-Hastingssplit-merge updateBayesian approach

Mathematics Subject Classification ID

Markov processes: estimation; hidden Markov models (62M05)

Related Items (1)

Selection of the number of clusters in functional data analysis

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Mixture models with an unknown number of components via a new posterior split-merge MCMC algorithm
• On Bayesian model and variable selection using MCMC
• Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions
• Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems
• Estimating the dimension of a model
• Model-based clustering of high-dimensional data: a review
• Mixture models, latent variables and partitioned importance sampling
• Markov chain Monte Carlo methods and the label switching problem in Bayesian mixture modeling
• Finite mixture and Markov switching models.
• A Bayesian analysis of some nonparametric problems
• Model-Based Gaussian and Non-Gaussian Clustering
• Practical Bayesian Density Estimation Using Mixtures of Normals
• Model-Based Clustering, Discriminant Analysis, and Density Estimation
• Dealing With Label Switching in Mixture Models
• Computational and Inferential Difficulties with Mixture Posterior Distributions
• Bayesian Measures of Model Complexity and Fit
• Bayesian Density Estimation and Inference Using Mixtures
• Bayesian analysis of outlier problems using divergence measures
• Partitioning gene expression data by data-driven Markov chain Monte Carlo
• On Information and Sufficiency
• A new look at the statistical model identification

This page was built for publication: A data-driven selection of the number of clusters in the Dirichlet allocation model via Bayesian mixture modelling