Inference for the Number of Topics in the Latent Dirichlet Allocation Model via Bayesian Mixture Modeling

DOI10.1080/10618600.2018.1558063OpenAlexW2905582305MaRDI QIDQ3391266

Hani Doss, Zhe Chen

Publication date: 28 March 2022

Published in: Journal of Computational and Graphical Statistics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/10618600.2018.1558063

zbMATH Keywords

Markov chain Monte Carlomixture modeluniform ergodicitylatent Dirichlet allocationtopic modeling

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (1)

Inference for the Number of Topics in the Latent Dirichlet Allocation Model via Bayesian Mixture Modeling

Uses Software

• R
• gensim
• Rcpp
• RcppArmadillo
• MALLET

Cites Work

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• Inference for the Number of Topics in the Latent Dirichlet Allocation Model via Bayesian Mixture Modeling
• Hierarchical Dirichlet Processes
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• Covariance structure of the Gibbs sampler with applications to the comparisons of estimators and augmentation schemes
• 10.1162/jmlr.2003.3.4-5.993
• Modelling Spatially Correlated Data via Mixtures: A Bayesian Approach

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