Distance dependent Chinese restaurant processes

Journal of Machine Learning Research

Volumn 12, Issue , 2011, Pages 2461-2488

  Blei, David M ^a   Frazier, Peter I ^b  

^a Princeton University   (United States)

^b Cornell University   (United States)

Author keywords

Bayesian nonparametrics; Chinese restaurant processes

Indexed keywords

BAYESIAN NONPARAMETRICS; CHINESE RESTAURANT PROCESSES; CLASS OF DISTRIBUTIONS; CLUSTERING MODEL; EMPIRICAL PERFORMANCE; GIBBS SAMPLERS; GIBBS SAMPLING; MIXTURE MODEL; NETWORK CONNECTIVITY; NETWORK DATA; NON-PARAMETRIC; SEQUENTIAL DATA; TEXT CORPORA;

MIXTURES;

EID: 80053537814     PISSN: 15324435     EISSN: 15337928     Source Type: Journal    
DOI: None     Document Type: Article

References (40)

1. A. Ahmed and E. Xing. Dynamic non-parametric mixture models and the recurrent Chinese restaurant process with applications to evolutionary clustering. In International Conference on Data Mining, 2008.
2. C. Antoniak. Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. The Annals of Statistics, 2(6):1152-1174, 1974.
3. D. Blackwell. Discreteness of Ferguson selections. The Annals of Statistics, 1(2):356-358, 1973.
4. D. Blei and P. Frazier. Distance dependent Chinese restaurant processes. In International Conference on Machine Learning, 2010.
5. D. Blei and M. Jordan. Variational inference for Dirichlet process mixtures. Journal of Bayesian Analysis, 1(1):121-144, 2005.
6. D. Blei, T. Griff ths, and M. Jordan. The nested Chinese restaurant process and Bayesian nonparametric inference of topic hierarchies. Journal of the ACM, 57(2):1-30, 2010.
7. D. B. Dahl. Distance-based probability distribution for set partitions with applications to Bayesian nonparametrics. In JSM Proceedings. Section on Bayesian Statistical Science, American Statistical Association, Alexandria, Va, 2008.
8. H. Daume. Fast search for Dirichlet process mixture models. In Artificial Intelligence and Statistics, San Juan, Puerto Rico, 2007. URL http://pub.hal3.name/#daume07astar-dp.
9. J. Duan, M. Guindani, and A. Gelfand. Generalized spatial Dirichlet process models. Biometrika, 94:809-825, 2007.
10. D. Dunson. Bayesian dynamic modeling of latent trait distributions. Biostatistics, 2006.
11. D. Dunson, N. Pillai, and J. Park. Bayesian density regression. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 69(2):163-183, 2007.
12. M. Escobar and M. West. Bayesian density estimation and inference using mixtures. Journal of the American Statistical Association, 90:577-588, 1995.
13. T. Ferguson. A Bayesian analysis of some nonparametric problems. The Annals of Statistics, 1:209-230, 1973.
14. E. Fox, E. Sudderth, M. Jordan, and A. Willsky. Developing a tempered HDP-HMM for systems with state persistence. Technical report, MIT Laboratory for Information and Decision Systems, 2007.
15. C. Geyer and E. Thompson. Constrained Monte Carlo maximum likelihood for dependent data. Journal of the American Statistical Association, 54(657-699), 1992.
16. S. Goldwater, T. Griffths, and M. Johnson. Interpolating between types and tokens by estimating power-law generators. In Neural Information Processing Systems, 2006.
17. J. Griffn and M. Steel. Order-based dependent Dirichlet processes. Journal of the American Statistical Association, 101(473):179-194, 2006. (Pubitemid 43500034)
18. J. A. Hartigan. Partition models. Communications in Statistics-Theory and Methods, 19(8):2745-2756, 1990.
19. M. Johnson, T. Griffths, and Goldwater S. Adaptor grammars: A framework for specifying compositional nonparametric Bayesian models. In B. Schölkopf, J. Platt, and T. Hoffman, editors, Advances in Neural Information Processing Systems 19, pages 641-648, Cambridge, MA, 2007. MIT Press.
20. R. Kass and A. Raftery. Bayes factors. Journal of the American Statistical Association, 90(430):773-795, 1995.
21. W. Li, D. Blei, and A. McCallum. Nonparametric Bayes pachinko allocation. In The 23rd Conference on Uncertainty in Artificial Intelligence, 2007.
22. P. Liang, M. Jordan, and B. Taskar. A permutation-augmented sampler for DP mixture models. In International Conference on Machine Learning, 2007.
23. S. MacEachern. Dependent nonparametric processes. In ASA Proceedings of the Section on Bayesian Statistical Science, 1999.
24. A. McCallum, K. Nigam, J. Rennie, and K. Seymore. Automating the construction of internet portals with machine learning. Information Retrieval, 2000.
25. K. T. Miller, T. L. Griffiths, and M. I. Jordan. The phylogenetic indian buffet process: A nonexchangeable nonparametric prior for latent features. In David A. McAllester and Petri Myllymäki, editors, UAI, pages 403-410. AUAI Press, 2008.
26. P. Mueller and F. Quintana. Random partition models with regression on covariates. In International Conference on Interdisciplinary Mathematical and Statistical Techniques, 2008.
27. P. Muller, F. Quintana, and G. Rosner. Bayesian clustering with regression. Working paper, 2008.
28. R. Neal. Probabilistic inference using Markov chain Monte Carlo methods. Technical Report CRG-TR-93-1, Department of Computer Science, University of Toronto, 1993.
29. R. Neal. Markov chain sampling methods for Dirichlet process mixture models. Journal of Computational and Graphical Statistics, 9(2):249-265, 2000.
30. J. Pitman. Combinatorial Stochastic Processes. Lecture Notes for St. Flour Summer School. Springer-Verlag, New York, NY, 2002.
31. C. Rasmussen and Z. Ghahramani. Infnite mixtures of Gaussian process experts. In T. Dietterich, S. Becker, and Z. Ghahramani, editors, Advances in Neural Information Processing Systems 14, Cambridge, MA, 2002. MIT Press.
32. C. Ritter and M. Tanner. Facilitating the Gibbs sampler: The Gibbs stopper and the Griddy-Gibbs sampler. Journal of the American Statistical Association, 87(419):861-868, 1992.
33. C. Robert and G. Casella. Monte Carlo Statistical Methods. Springer Texts in Statistics. Springer-Verlag, New York, NY, 2004.
34. E. Sudderth, A. Torralba, W. Freeman, and A. Willsky. Describing visual scenes using transformed Dirichlet processes. In Advances in Neural Information Processing Systems 18, 2005.
35. E. B. Sudderth and M. I. Jordan. Shared segmentation of natural scenes using dependent pitman-yor processes. In Daphne Koller, Dale Schuurmans, Yoshua Bengio, and Léon Bottou, editors, NIPS, pages 1585-1592. MIT Press, 2008.
36. Y. Teh. A hierarchical Bayesian language model based on Pitman-Yor processes. In Proceedings of the Association of Computational Linguistics, 2006.
37. Y. Teh, M. Jordan, M. Beal, and D. Blei. Hierarchical Dirichlet processes. Journal of the American Statistical Association, 101(476):1566-1581, 2006.
38. E. Xing, M. Jordan, and R. Sharan. Bayesian haplotype inference via the Dirichlet process. Journal of Computational Biology, 14(3):267-284, 2007. (Pubitemid 46742185)
39. Y. Xue, D. Dunson, and L. Carin. The matrix stick-breaking process for f exible multi-task learning. In International Conference on Machine Learning, 2007.
40. X. Zhu, Z. Ghahramani, and J. Lafferty. Time-sensitive Dirichlet process mixture models. Technical Report CMU-CALD-05-104, Carnegie Mellon University, 2005.