References

Foundational papers

Dirichlet processes, Chinese restaurant processes:

• Antoniak, C. E. (1974). Mixtures of Dirichlet processes with applications to Bayesian nonparametric problems. Annals of Statistics, 2(6):1152–1174.
• Blackwell, D. and MacQueen, J. B. (1973). Ferguson distributions via Pólya urn schemes. Annals of Statistics, 1:353–355.
• Ferguson, T. S. (1973). A Bayesian analysis of some nonparametric problems. Annals of Statistics, 1(2):209–230.
• Lo, Albert Y. On a class of Bayesian nonparametric estimates. I. Density estimates. Annals of Statistics 12(1):351-357.
• Rasmussen, C. E. (2000). The infinite Gaussian mixture model. In Advances in Neural Information Processing Systems, volume 12.
• Sethuraman, J. (1994). A constructive definition of Dirichlet priors. Statistica Sinica, 4:639–650.

Hierarchical Dirichlet processes:

• Beal, M. J., Ghahramani, Z., and Rasmussen, C. E. (2002). The infinite hidden Markov model. In Advances in Neural Information Processing Systems, volume 14.
• Teh, Y. W., Jordan, M. I., Beal, M. J., and Blei, D. M. (2006). Hierarchical Dirichlet processes. Journal of the American Statistical Association, 101(476):1566–1581.

Beta processes, Indian buffet processes:

• Griffiths, T. L. and Ghahramani, Z. (2006). Infinite latent feature models and the Indian buffet process. In Advances in Neural Information Processing Systems, volume 18.
• Hjor t, N. L. (1990). Nonparametric Bayes estimators based on beta processes in models for life histor y data. Annals of Statistics, 18(3):1259–1294.
• Teh, Y. W., Görür, D., and Ghahramani, Z. (2007). Stick-breaking construction for the Indian buffet process. In Proceedings of the International Conference on Ar tificial Intelligence and Statistics, volume 11.
• Thibaux, R. and Jordan, M. I. (2007). Hierarchical beta processes and the Indian buffet process. In Proceedings of the International Workshop on Ar tificial Intelligence and Statistics, volume 11.

Trees:

• Kingman, J. F. C. (1982b). On the genealogy of large populations. Journal of Applied Probability, 19:27–43. Essays in Statistical Science.
• Neal, R. M. (2003). Density modeling and clustering using Dirichlet diffusion trees. In Bayesian Statistics, volume 7, pages 619–629.

Inference

Markov chain Monte Carlo:

• Fearnhead, P. (2004). Particle filters for mixture models with an unknown number of components. Statistics and Computing, 14:11–21.
• Ishwaran, H. and James, L. F. (2001). Gibbs sampling methods for stick-breaking priors. Journal of the American Statistical Association, 96(453):161–173.
• Jain, S. and Neal, R. M. (2004). A split-merge Markov chain Monte Carlo procedure for the Dirichlet process mixture model. Technical repor t, Depar tment of Statistics, University of Toronto.
• Liang, P., Jordan, M. I., and Taskar, B. (2007a). A permutation-augmented sampler for Dirichlet process mixture models. In Proceedings of the International Conference on Machine Learning.
• Neal, R. M. (2000). Markov chain sampling methods for Dirichlet process mixture models. Journal of Computational and Graphical Statistics, 9:249–265.

Variational:

• Blei, D. M. and Jordan, M. I. (2006). Variational inference for Dirichlet process mixtures. Bayesian Analysis, 1(1):121–144.
• Kurihara, K., Welling, M., and Vlassis, N. (2007). Accelerated variational DP mixture models. In Advances in Neural Information Processing Systems, volume 19.
• Kurihara, K., Welling, M., and Teh, Y. W. (2007). Collapsed Variational Dirichlet Process Mixture Models. In Proceedings of the International Joint Conference on Artificial Intelligence.
• Teh, Y. W., Kurihara, K., Welling, M. (2008). Collapsed Variational Inference for HDP. In Advances in Neural Information Processing Systems, volume 20.

Others:

• Minka, T. P. and Ghahramani, Z. (2003). Expectation propagation for infinite mixtures. Presented at NIPS2003 Workshop on Nonparametric Bayesian Methods and Infinite Models.
• Heller, K. A. and Ghahramani, Z. (2005). Bayesian hierarchical clustering. In Proceedings of the International Conference on Machine Learning, volume 22.
• Daume III, H. (2007). Fast search for Dirichlet process mixture models. In Proceedings of the International Workshop on Ar tificial Intelligence and Statistics, volume 11.

Applications

Natural language processing and computational linguistics:

• Finkel, J. R., Grenager, T., and Manning, C. D. (2007). The infinite tree. In Proceedings of the Annual Meeting of the Association for Computational Linguistics.
• Goldwater, S. (2006). Nonparametric Bayesian Models of Lexical Acquisition. PhD thesis, Brown University.
• Goldwater, S., Griffiths, T., and Johnson, M. (2006a). Inter polating between types and tokens by estimating power-law generators. In Advances in Neural Information Processing Systems, volume 18.
• Goldwater, S., Griffiths, T. L., and Johnson, M. (2006b). Contextual dependencies in unsuper vised word segmentation. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics.
• Haghighi, A. and Klein, D. (2007). Unsuper vised coreference resolution in a nonparametric Bayesian model. In Proceedings of the Annual Meeting of the Association for Computational Linguistics.
• Johnson, M., Griffiths, T. L., and Goldwater, S. (2007). Adaptor grammars: A framework for specifying compositional nonparametric Bayesian models. In Advances in Neural Information Processing Systems, volume 19.
• Liang, P., Petrov, S., Jordan, M. I., and Klein, D. (2007b). The infinite PCFG using hierarchical Dirichlet processes. In Proceedings of the Conference on Empirical Methods in Natural Language Processing.
• Teh, Y. W. (2006b). A hierarchical Bayesian language model based on Pitman-Yor processes. In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, pages 985–992.

Document models and information retrieval:

• Blei, D., Griffiths, T., Jordan, M., and Tenenbaum, J. (2004). Hierarchical topic models and the nested Chinese restaurant process. In Advances in Neural Information Processing Systems, volume 16.
• Cowans, P. (2004). Information retrieval using hierarchical Dirichlet processes. In Proceedings of the Annual International Conference on Research and Development in Information Retrieval, volume 27, pages 564–565.
• Cowans, P. (2006). Probabilistic Document Modelling. PhD thesis, University of Cambridge.
• Li, W., Blei, D., and McCallum, A. (2007). Nonparametric Bayes pachinko allocation. In Proceedings of the Conference on Uncertainty in Artificial Intelligence.

Signal process, target tracking:

• Fox, E. B., Choi, D. S., and Willsky, A. S. (2006). Nonparametric Bayesian methods for large scale multi-target tracking. In Proceedings of the Asilomar Conference on Signals, Systems, and Computers, volume 40.
• Fox, E. B., Sudderth, E. B., and Willsky, A. S. (2007b). Hierarchical Dirichlet processes for tracking maneuvering targets. In Proceedings of the International Conference on Information Fusion.

Cognitive science and systems:

• Görür, D., Jäkel, F., and Rasmussen, C. E. (2006). A choice model with infinitely many latent features. In Proceedings of the International Conference on Machine Learning, volume 23.
• Griffiths, T. L., Canini, K. R., Sanborn, A. N., and Navarro, D. J. (2007a). Unifying rational models of categorization via the hierarchical Dirichlet process. In Proceedings of the Annual Conference of the Cognitive Science Society, volume 29.
• Roy, D. M., Kemp, C., Mansinghka, V., and Tenenbaum, J. B. (2007). Learning annotated hierarchies from relational data. In Advances in Neural Information Processing Systems, volume 19.
• Shafto, P., Kemp, C., Mansinghka, V., Gordon, M., and Tenenbaum, J. B. (2006). Learning cross-cutting systems of categories. In Proceedings of the Annual Conference of the Cognitive Science Society, volume 28.

Relational and spatial models:

• Kemp, C., Tenenbaum, J. B., Griffiths, T. L., Yamada, T., and Ueda, N. (2006). Learning systems of concepts with an infinite relational model. In Proceedings of the AAAI Conference on Ar tificial Intelligence, volume 21.
• MacEachern, S., Kottas, A., and Gelfand, A. (2001). Spatial nonparametric Bayesian models. Technical Report 01-10, Institute of Statistics and Decision Sciences, Duke University.
• Meeds, E., Ghahramani, Z., Neal, R. M., and Roweis, S. T. (2007). Modeling dyadic data with binar y latent factors. In Advances in Neural Information Processing Systems, volume 19.
• Xu, Z., Tresp, V., Yu, K., and Kriegel, H.-P. (2006). Infinite hidden relational models. In Proceedings of the Conference on Uncer tainty in Ar tificial Intelligence, volume 22.

Visual systems, image processing:

• Kim, S. and Smyth, P. (2007). Hierarchical dirichlet processes with random effects. In Advances in Neural Information Processing Systems, volume 19.
• Kivinen, J., Sudder th, E., and Jordan, M. I. (2007). Image denoising with nonparametric hidden Markov trees. In International Conference on Image Processing.
• Sudderth, E., Torralba, A., Freeman, W., and Willsky, A. (2006a). Depth from familiar objects: A hierarchical model for 3D scenes. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition.
• Sudderth, E., Torralba, A., Freeman, W., and Willsky, A. (2006b). Describing visual scenes using transformed Dirichlet processes. In Advances in Neural Information Processing Systems, volume 18.
• Sudderth, E., Torralba, A., Freeman, W., and Willsky, A. (2007). Describing visual scenes using transformed objects and parts. To appear in the International Journal of Computer Vision.

Computational biology, neural data analysis:

• Teh, Y. W., Daume III, H., and Roy, D. M. (2008). Bayesian agglomerative clustering with coalescents. In Advances in Neural Information Processing Systems, volume 20.
• Wood, F., Goldwater, S., and Black, M. J. (2006a). A non-parametric Bayesian approach to spike sorting. In Proceedings of the IEEE Conference on Engineering in Medicine and Biologicial Systems, volume 28.
• Xing, E., Sharan, R., and Jordan, M. (2004). Bayesian haplotype inference via the dirichlet process. In Proceedings of the International Conference on Machine Learning, volume 21.
• Xing, E. P., Jordan, M. I., and Roded, R. (2007). Bayesian haplotype inference via the Dirichlet process. Journal of Computational Biology, 14(3):267–284.
• Xing, E. P. and Sohn, K. (2007a). Hidden Markov Dirichlet process: Modeling genetic recombination in open ancestral space. Bayesian Analysis, 2(2).
• Xing, E. P. and Sohn, K. (2007b). A nonparametric Bayesian approach for haplotype reconstruction from single and multi-population data. Technical Repor t CMU-MLD 07-107, Carnegie Mellow University.
• Xing, E. P., Sohn, K., Jordan, M. I., and Teh, Y. W. (2006). Bayesian multi-population haplotype inference via a hierarchical Dirichlet process mixture. In Proceedings of the International Conference on Machine Learning, volume 23.