Dynamic hierarchical Dirichlet processes topic model using the power prior approach

Author:

Jeong Kuhwan,Kim Yongdai

Funder

National Research Foundation of Korea

Publisher

Springer Science and Business Media LLC

Subject

Statistics and Probability

Reference11 articles.

1. Ahmed, A., & Xing, E.P., (2010). Timeline: A dynamic hierarchical Dirichlet process model for recovering birth/death and evolution of topics in text stream. In: Proceedings of the 26th conference on uncertainty in airtificial intelligence (pp. 20–29).

2. Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent Dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.

3. Fu, X., Li, J., Yang, K., Cui, L., & Yang, L. (2016). Dynamic online HDP model for discovering evolutionary topics from Chinese social texts. Neurocomputing, 171, 412–424.

4. Ibrahim, J. G., & Chen, M. H. (2000). Power prior distributions for regression models. Statistical Science, 15(1), 46–60.

5. Ibrahim, J. G., Chen, M. H., Gwon, Y., & Chen, F. (2015). The power prior: Theory and applications. Statistics in Medicine, 34(28), 3724–3749.

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