References
- Andrews, M., and G. Vigliocco. 2010. The hidden Markov Topic model: A probabilistic model of semantic representation. Topics in Cognitive Science 2 (1):101–13. doi:10.1111/j.1756-8765.2009.01074.x. 25163624
- Arbel, J., P. De Blasi, and I. Prünster. 2019. Stochastic approximations to the Pitman-Yor process. Bayesian Analysis 14 (4):1201–19.
- Blei, D. M., Alp. Kucukelbir, and J. D. McAuliffe. 2017. Variational inference: A review for statisticians. Journal of the American Statistical Association 112 (518):859–77. doi:10.1080/01621459.2017.1285773.
- Blei, D. M., and J. D. Lafferty. 2006. Correlated topic models. In Advances in Neural Information Processing Systems, ed. Y. Weiss, B. Schölkopf, and J. Platt, Vol. 18. Cambridge, MA: MIT Press.
- Blei, D. M., A. Y. Ng, J, and M. I. Jordan. 2003. Latent dirichlet allocation. Journal of Machine Learning Research 3:993–1022.
- Debole, F., and F. Sebastiani. 2005. An analysis of the relative hardness of Reuters-21578 subsets. Journal of the American Society for Information Science and Technology 56 (6):584–96. doi:10.1002/asi.20147.
- Dempster, A. P., N. M. Laird, and D. B. Rubin. 1977. Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series B: Statistical Methodology 39 (1):1–22. doi:10.1111/j.2517-6161.1977.tb01600.x.
- Ferguson, T. S. 1973. A Bayesian analysis of some nonparametric problems. The Annals of Statistics 1 (2):209–30.
- Goldwater, S., M. Johnson, and T. Griffiths. 2006. Interpolating between types and tokens by estimating power-law generators. In Advances in Neural Information Processing Systems, ed. Y. Weiss, B. Schölkopf, and J. Platt, Vol. 18. Cambridge, MA: MIT Press.
- Griffiths, T.,M. Steyvers, D. Blei, and J. Tenenbaum. 2005. Integrating topics and syntax. In Advances in Neural Information Processing Systems, ed. L. Saul, Y. Weiss, and L. Bottou, Vol. 17. Cambridge, MA: MIT Press.
- Ishwaran, H., and L. F. James. 2001. Gibbs sampling methods for stick-breaking priors. Journal of the American Statistical Association 96 (453):161–73. doi:10.1198/016214501750332758.
- Jordan, M. I., Z. Ghahramani, T. S. Jaakkola, and L. K. Saul. 1999. An introduction tovariational methods for graphical models. Machine Learning 37 (2):183–233. doi:10.1023/A:1007665907178.
- Lim, K. W., W. Buntine, C. Chen, and Lan. Du. 2016. Nonparametric Bayesian topic modelling with the hierarchical Pitman-Yor processes. International Journal of Approximate Reasoning 78:172–91. doi:10.1016/j.ijar.2016.07.007.
- Lindsey, R.W. Headden, and M. Stipicevic. 2012. A phrase-discovering topic model using hierarchical Pitman-Yor processes. Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 214– 22. Jeju Island, Korea: Association for Computational Linguistics.
- Neal, R. M., and G. E. Hinton. 1998. A view of the EM algorithm that justifies incremental, sparse, and other variants. In Learning in graphical models, 355– 68. Dordrecht: Springer.
- Sato, I. and H. Nakagawa, 2010. Topic models with power-law using Pitman-Yor process. Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 673–82. New York, NY, USA: Association for Computing Machinery.
- Sebastiani, F. 2002. Machine learning in automated text categorization. ACM Computing Surveys 34 (1):1–47. doi:10.1145/505282.505283.
- Teh, Y. W., M. I. Jordan, M. J. Beal, and D. M. Blei. 2006. Hierarchical Dirichlet processes. Journal of the American Statistical Association 101 (476):1566–81. doi:10.1198/016214506000000302.
- Wainwright, M. J., and M. I. Jordan. 2008. Graphical models, exponential families, and variational inference. Hanover: Now Publishers.
- Wallach, H. M. 2006. Topic modeling: Beyond bag-of-words. Proceedings of the 23rd International Conference on Machine Learning, 977– 84. New York, NY, USA: Association for Computing Machinery.
- Wang, B., and D. Titterington. 2006. Convergence properties of a general algorithm for calculating variational Bayesian estimates for a normal mixture model. Bayesian Analysis 1 (3):625–50.
- Wang, X., and A. Mccallum. 2006. Topics over time: A non-Markov continuous-time model of topical trends, Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 424– 33. New York, NY, USA: Association for Computing Machinery.