Variational inference for the nested Chinese restaurant process

Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference

Volumn , Issue , 2009, Pages 1990-1998

  Wang, Chong ^a   Blei, David M ^a  

^a Princeton University   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; BAYESIAN NETWORKS;

'CURRENT; BREAKINGS; INFERENCE METHODS; INFERENCE TECHNIQUES; MCMC SAMPLING; NON-PARAMETRIC BAYESIAN MODELING; POSTERIOR DISTRIBUTIONS; TREE-BASED; VARIATIONAL INFERENCE; VARIATIONAL METHODS;

TREES (MATHEMATICS);

EID: 84863338282     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (26)

1. Blei, D. M., T. L. Griffiths, M. I. Jordan, et al. Hierarchical topic models and the nested Chinese restaurant process. In NIPS. 2003.
2. Bart, E., I. Porteous, P. Perona, et al. Unsupervised learning of visual taxonomies. In CVPR. 2008.
3. Sivic, J., B. C. Russell, A. Zisserman, et al. Unsupervised discovery of visual object class hierarchies. In CVPR. 2008.
4. Aldous, D. Exchangeability and related topics. In Ecole d'Ete de Probabilities de Saint-Flour XIII 1983, pages 1-198. Springer, 1985.
5. Ferguson, T. S. A Bayesian analysis of some nonparametric problems. The Annals of Statistics, 1(2):209-230, 1973.
6. Neal, R. Probabilistic inference using Markov chain Monte Carlo methods. Tech. Rep. CRG-TR-93-1, Department of Computer Science, University of Toronto, 1993.
7. Robert, C., G. Casella. Monte Carlo Statistical Methods. Springer-Verlag, New York, NY, 2004.
8. Jordan, M. I., Z. Ghahramani, T. S. Jaakkola, et al. An introduction to variational methods for graphical models. Learning in Graphical Models, 1999.
9. Blei, D. M., M. I. Jordan. Variational methods for the Dirichlet process. In ICML. 2004.
10. Kurihara, K., M. Welling, N. A. Vlassis. Accelerated variational Dirichlet process mixtures. In NIPS. 2006.
11. Kurihara, K., M. Welling, Y. W. Teh. Collapsed variational Dirichlet process mixture models. In IJCAI. 2007.
12. Teh, Y. W., K. Kurihara, M. Welling. Collapsed variational inference for HDP. In NIPS. 2008.
13. Sudderth, E. B., M. I. Jordan. Shared segmentation of natural scenes using dependent Pitman-Yor processes. In NIPS. 2008.
14. Doshi, F., K. T. Miller, J. Van Gael, et al. Variational inference for the Indian buffet process. In AISTATS, vol. 12. 2009.
15. Escobar, M. D., M. West. Bayesian density estimation and inference using mixtures. Journal of the American Statistical Association, 90:577-588, 1995.
16. Tipping, M. E., C. M. Bishop. Probabilistic principal component analysis. Journal of the Royal Statistical Society, Series B, 61:611-622, 1999.
17. Bishop, C. M. Variational principal components. In ICANN. 1999.
18. Collins, M., S. Dasgupta, R. E. Schapire. A generalization of principal components analysis to the exponential family. In NIPS. 2001.
19. Mohamed, S., K. A. Heller, Z. Ghahramani. Bayesian exponential family PCA. In NIPS. 2008.
20. Bach, F. R., M. I. Jordan. Beyond independent components: Trees and clusters. JMLR, 4:1205-1233, 2003.
21. Antoniak, C. E. Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. The Annals of Statistics, 2(6):1152-1174, 1974.
22. Sethuraman, J. A constructive definition of Dirichlet priors. Statistica Sinica, 4:639-650, 1994.
23. Wainwright, M., M. Jordan. Variational inference in graphical models: The view from the marginal polytope. In Allerton Conference on Control, Communication and Computation. 2003.
24. Ueda, N., R. Nakano, Z. Ghahramani, et al. SMEM algorithm for mixture models. Neural Computation, 12(9):2109-2128, 2000.
25. Griffiths, T. L., M. Steyvers. Finding scientific topics. Proc Natl Acad Sci USA, 101 Suppl 1:5228-5235, 2004.
26. Tipping, M. E., C. M. Bishop. Mixtures of probabilistic principal component analysers. Neural Computation, 11(2):443-482, 1999.