Message Passing Algorithms for the Dirichlet Diffusion Tree
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Summary
This work provides the first deterministic approximate inference methods for DDT models and shows excellent performance compared to the MCMC alternative on a density estimation problem, and significantly outperforms kernel density estimators.
- Type
- article
- Published
- 2011-01-01
- Cited by
- 4
- References
- 25
- OpenAlex
- https://openalex.org/W36988065
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:594200
Keywords
Computer science, Tree (set theory), Message passing, Latent Dirichlet allocation, Diffusion
References
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- Variational Message Passing
- A MCMC Approach to Hierarchical Mixture Modelling
- Bayesian Agglomerative Clustering with Coalescents
- Learning annotated hierarchies from relational data
- Density Modeling and Clustering Using Dirichlet Diffusion Trees
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- Beta diffusion trees and hierarchical feature allocations
- DenPEHC: Density peak based efficient hierarchical clustering
- TREE-REGULARIZED BAYESIAN LATENT CLASS ANALYSIS FOR IMPROVING WEAKLY SEPARATED DIETARY PATTERN SUBTYPING IN SMALL-SIZED SUBPOPULATIONS
- Warped Diffusion for Latent Differentiation Inference
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