Variational Bayes estimation of hierarchical Dirichlet-multinomial mixtures for text clustering
Author
Abstract
Suggested Citation
DOI: 10.1007/s00180-023-01350-8
Download full text from publisher
As the access to this document is restricted, you may want to search for a different version of it.
References listed on IDEAS
- David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
- A. Pollice & M. Bilancia, 2000. "A hierarchical finite mixture model for Bayesian classification in the presence of auxiliary information," Metron - International Journal of Statistics, Dipartimento di Statistica, Probabilità e Statistiche Applicate - University of Rome, vol. 0(3-4), pages 109-131.
- repec:dau:papers:123456789/3692 is not listed on IDEAS
- Zoubin Ghahramani, 2015. "Probabilistic machine learning and artificial intelligence," Nature, Nature, vol. 521(7553), pages 452-459, May.
- Matthew Stephens, 2000. "Dealing with label switching in mixture models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 62(4), pages 795-809.
- Laura Anderlucci & Cinzia Viroli, 2020. "Mixtures of Dirichlet-Multinomial distributions for supervised and unsupervised classification of short text data," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(4), pages 759-770, December.
Citations
Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
Cited by:
- Massimo Bilancia & Andrea Nigri & Samuele Magro, 2025. "Stochastic variational inference for clustering short text data with finite mixtures of Dirichlet-Multinomial distributions," Statistical Papers, Springer, vol. 66(4), pages 1-39, June.
Most related items
These are the items that most often cite the same works as this one and are cited by the same works as this one.- Riccardo Rastelli & Michael Fop, 2020. "A stochastic block model for interaction lengths," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 14(2), pages 485-512, June.
- Diana Mindrila, 2023. "Bayesian Latent Class Analysis: Sample Size, Model Size, and Classification Precision," Mathematics, MDPI, vol. 11(12), pages 1-18, June.
- Yuan Fang & Dimitris Karlis & Sanjeena Subedi, 2022. "Infinite Mixtures of Multivariate Normal-Inverse Gaussian Distributions for Clustering of Skewed Data," Journal of Classification, Springer;The Classification Society, vol. 39(3), pages 510-552, November.
- Zhang, Jincheng & Zhao, Xiaowei & Jin, Siya & Greaves, Deborah, 2022. "Phase-resolved real-time ocean wave prediction with quantified uncertainty based on variational Bayesian machine learning," Applied Energy, Elsevier, vol. 324(C).
- Gao Wang & Abhishek Sarkar & Peter Carbonetto & Matthew Stephens, 2020. "A simple new approach to variable selection in regression, with application to genetic fine mapping," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 82(5), pages 1273-1300, December.
- Zaid Tashman & Christoph Gorder & Sonali Parthasarathy & Mohamad M. Nasr-Azadani & Rachel Webre, 2020. "Anomaly Detection System for Water Networks in Northern Ethiopia Using Bayesian Inference," Sustainability, MDPI, vol. 12(7), pages 1-16, April.
- O’Dwyer, Edward & Pan, Indranil & Acha, Salvador & Shah, Nilay, 2019. "Smart energy systems for sustainable smart cities: Current developments, trends and future directions," Applied Energy, Elsevier, vol. 237(C), pages 581-597.
- Massimo Bilancia & Andrea Nigri & Samuele Magro, 2025. "Stochastic variational inference for clustering short text data with finite mixtures of Dirichlet-Multinomial distributions," Statistical Papers, Springer, vol. 66(4), pages 1-39, June.
- Angela Maria D’Uggento & Albino Biafora & Fabio Manca & Claudia Marin & Massimo Bilancia, 2023. "A text data mining approach to the study of emotions triggered by new advertising formats during the COVID-19 pandemic," Quality & Quantity: International Journal of Methodology, Springer, vol. 57(3), pages 2303-2325, June.
- Allassonnière, Stéphanie & Chevallier, Juliette, 2021. "A new class of stochastic EM algorithms. Escaping local maxima and handling intractable sampling," Computational Statistics & Data Analysis, Elsevier, vol. 159(C).
- Masafumi Nakano & Akihiko Takahashi & Soichiro Takahashi, 2018. "State Space Approach to Adaptive Artificial Intelligence Modeling: Application to Financial Portfolio with Fuzzy System," CARF F-Series CARF-F-422, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
- Deborah Gefang & Gary Koop & Aubrey Poon, 2019.
"Variational Bayesian Inference in Large Vector Autoregressions with Hierarchical Shrinkage,"
Economic Statistics Centre of Excellence (ESCoE) Discussion Papers
ESCoE DP-2019-07, Economic Statistics Centre of Excellence (ESCoE).
- Deborah Gefang & Gary Koop & Aubrey Poon, 2019. "Variational Bayesian Inference in Large Vector Autoregressions with Hierarchical Shrinkage," CAMA Working Papers 2019-08, Centre for Applied Macroeconomic Analysis, Crawford School of Public Policy, The Australian National University.
- Deborah Gefang & Gary Koop & Aubrey Poon, 2019. "Variational Bayesian Inference in Large Vector Autoregressions with Hierarchical Shrinkage," Discussion Papers in Economics 19/05, Division of Economics, School of Business, University of Leicester.
- Korobilis, Dimitris & Koop, Gary, 2018.
"Variational Bayes inference in high-dimensional time-varying parameter models,"
Essex Finance Centre Working Papers
22665, University of Essex, Essex Business School.
- Koop, Gary & Korobilis, Dimitris, 2018. "Variational Bayes inference in high-dimensional time-varying parameter models," MPRA Paper 87972, University Library of Munich, Germany.
- Gary Koop & Dimitris Korobilis, 2018. "Variational Bayes inference in high-dimensional time-varying parameter models," Working Paper series 18-31, Rimini Centre for Economic Analysis.
- Dayuan Wu & Ping Yan & You Guo & Han Zhou & Jian Chen, 2022. "A gear machining error prediction method based on adaptive Gaussian mixture regression considering stochastic disturbance," Journal of Intelligent Manufacturing, Springer, vol. 33(8), pages 2321-2339, December.
- Wan-Lun Wang, 2019. "Mixture of multivariate t nonlinear mixed models for multiple longitudinal data with heterogeneity and missing values," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 28(1), pages 196-222, March.
- Mark S. Handcock & Adrian E. Raftery & Jeremy M. Tantrum, 2007. "Model‐based clustering for social networks," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 170(2), pages 301-354, March.
- Arman Oganisian & Nandita Mitra & Jason A. Roy, 2021. "A Bayesian nonparametric model for zero‐inflated outcomes: Prediction, clustering, and causal estimation," Biometrics, The International Biometric Society, vol. 77(1), pages 125-135, March.
- Shotwell Matthew S & Slate Elizabeth H, 2010. "Bayesian Modeling of Footrace Finishing Times," Journal of Quantitative Analysis in Sports, De Gruyter, vol. 6(3), pages 1-21, July.
- Shen Liu & Hongyan Liu, 2021. "Tagging Items Automatically Based on Both Content Information and Browsing Behaviors," INFORMS Journal on Computing, INFORMS, vol. 33(3), pages 882-897, July.
- Dimitris Korobilis & Kenichi Shimizu, 2022.
"Bayesian Approaches to Shrinkage and Sparse Estimation,"
Foundations and Trends(R) in Econometrics, now publishers, vol. 11(4), pages 230-354, June.
- Korobilis, Dimitris & Shimizu, Kenichi, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," MPRA Paper 111631, University Library of Munich, Germany.
- Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Papers 2021_19, Business School - Economics, University of Glasgow.
- Dimitris Korobilis & Kenichi Shimizu, 2022. "Bayesian Approaches to Shrinkage and Sparse Estimation," Working Paper series 22-02, Rimini Centre for Economic Analysis.
- Dimitris Korobilis & Kenichi Shimizu, 2021. "Bayesian Approaches to Shrinkage and Sparse Estimation," Papers 2112.11751, arXiv.org.
More about this item
Keywords
Text clustering; Finite mixture models; Dirichlet-multinomial distribution; Bayesian hierarchical modelling; Variational inference;All these keywords.
Statistics
Access and download statisticsCorrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:compst:v:38:y:2023:i:4:d:10.1007_s00180-023-01350-8. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .
Please note that corrections may take a couple of weeks to filter through the various RePEc services.