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[2602.18795] Vectorized Bayesian Inference for Latent Dirichlet-Tree Allocation
Summary
This paper presents a novel framework, Latent Dirichlet-Tree Allocation (LDTA), which enhances the traditional Latent Dirichlet Allocation (LDA) by incorporating a Dirichlet-Tree distribution for improved topic modeling.
Why It Matters
The introduction of LDTA addresses limitations in LDA by allowing for richer hierarchical relationships among topics, making it a significant advancement in Bayesian inference methods for topic modeling. This could lead to better performance in applications requiring nuanced understanding of data structures.
Key Takeaways
- LDTA generalizes LDA by using a Dirichlet-Tree distribution for topic proportions.
- The framework maintains LDA's generative structure while enhancing modeling capacity.
- Universal mean-field variational inference and Expectation Propagation are developed for efficient inference.
- Vectorized implementations allow for GPU acceleration, improving computational efficiency.
- This advancement could significantly impact fields requiring complex topic modeling.
Computer Science > Machine Learning arXiv:2602.18795 (cs) [Submitted on 21 Feb 2026] Title:Vectorized Bayesian Inference for Latent Dirichlet-Tree Allocation Authors:Zheng Wang, Nizar Bouguila View a PDF of the paper titled Vectorized Bayesian Inference for Latent Dirichlet-Tree Allocation, by Zheng Wang and Nizar Bouguila View PDF HTML (experimental) Abstract:Latent Dirichlet Allocation (LDA) is a foundational model for discovering latent thematic structure in discrete data, but its Dirichlet prior cannot represent the rich correlations and hierarchical relationships often present among topics. We introduce the framework of Latent Dirichlet-Tree Allocation (LDTA), a generalization of LDA that replaces the Dirichlet prior with an arbitrary Dirichlet-Tree (DT) distribution. LDTA preserves LDA's generative structure but enables expressive, tree-structured priors over topic proportions. To perform inference, we develop universal mean-field variational inference and Expectation Propagation, providing tractable updates for all DT. We reveal the vectorized nature of the two inference methods through theoretical development, and perform fully vectorized, GPU-accelerated implementations. The resulting framework substantially expands the modeling capacity of LDA while maintaining scalability and computational efficiency. Comments: Subjects: Machine Learning (cs.LG); Machine Learning (stat.ML) Cite as: arXiv:2602.18795 [cs.LG] (or arXiv:2602.18795v1 [cs.LG] for this version) htt...