Hierarchical Dirichlet Processes

Yee Whye Teh, Michael I Jordan, Matthew J Beal et al., 2006. Journal of the American Statistical Association.

Introduces the hierarchical Dirichlet process, a nonparametric Bayesian model in which groups of data share mixture components and the number of components is learned from the data. It underlies nonparametric topic models.

Voynich ManuscriptArticledoi: 10.1198/016214506000000302Checked against the original0

What it's used for here

The inductive-methods survey lists it among the topic-modelling references.

Cited by on this site 1 record

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Inductive methods

SRC

Cite it

Teh, Y. W., Jordan, M. I., Beal, M. J., & Blei, D. M. (2006). Hierarchical Dirichlet Processes. Journal of the American Statistical Association. https://doi.org/10.1198/016214506000000302

@article{teh2006hierarchical,
  author = {Teh, Yee Whye and Jordan, Michael I and Beal, Matthew J and Blei, David M},
  title = {Hierarchical Dirichlet Processes},
  journal = {Journal of the American Statistical Association},
  year = {2006},
  doi = {10.1198/016214506000000302},
  url = {https://doi.org/10.1198/016214506000000302}
}

TY  - JOUR
TI  - Hierarchical Dirichlet Processes
AU  - Teh, Yee Whye
AU  - Jordan, Michael I
AU  - Beal, Matthew J
AU  - Blei, David M
PY  - 2006
JO  - Journal of the American Statistical Association
DO  - 10.1198/016214506000000302
UR  - https://doi.org/10.1198/016214506000000302
ER  -

Teh, Yee Whye et al.. “Hierarchical Dirichlet Processes.” Journal of the American Statistical Association (2006). https://doi.org/10.1198/016214506000000302.