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Y. W. Teh , D. Görür and Z. Ghahramani. 2007. Stick-breaking Construction for the Indian Buffet Process. In Proceedings of the International Conference on Artificial Intelligence and Statistics, vol 11. [Dirichlet, npbayes] pdf google scholar
R. Thibaux and M.I. Jordan. 2007. Hierarchical beta processes and the Indian buffet process. In Proceedings of the International Workshop on Artificial Intelligence and Statistics, vol 11. [Dirichlet, npbayes] pdf google scholar
Y. W. Teh . 2007. Dirichlet Processes. Submitted to Encyclopedia of Machine Learning. [Dirichlet, npbayes] pdf google scholar
Volker Tresp. 2006. Dirichlet Processes and Nonparametric Bayesian Modelling. [Bayes, Nonparametric, Dirichlet, npbayes] pdf google scholar
Y. W. Teh , M. I. Jordan , M. J. Beal and D. M. Blei . 2006. Hierarchical Dirichlet Processes. Journal of the American Statistical Association, vol 101, no 476, pp 1566-1581. [Dirichlet, cl09bib, npbayes, comp542] pdf pdf pdf google scholar
Matthias Seeger. 2006. Bayesian Modelling for Data Analysis and Learning from Data. Notes and slides on the course held at IK 2006. [Bayes, npbayes] pdf google scholar
D.M. Blei and M.I. Jordan. 2006. Variational inference for Dirichlet process mixtures. Bayesian Analysis, vol 1, no 1, pp 121--144. [Dirichlet, npbayes] pdf google scholar
Hal Daume III. 2006. Beyond EM: Bayesian Techniques for HLT. HLT-NAACL '06 Tutorial. [Bayes, npbayes] pdf google scholar
Matthias Seeger. 2005. Gaussian Processes for Machine Learning: Where Are We and Where Could We Go?. [Bayes, Nonparametric, npbayes] pdf google scholar
Michael I. Jordan. 2005. Dirichlet Processes, Chinese Restaurant Processes and all that. NIPS Tutorial. [Bayes, Nonparametric, Dirichlet, npbayes] pdf ps google scholar
Y. W. Teh , M. I. Jordan , M. J. Beal and D. M. Blei . 2005. Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes. In Advances in Neural Information Processing Systems, vol 17. [Dirichlet, npbayes] pdf google scholar
Matthias Seeger. 2005. Bayesian Gaussian Process Models: PAC-Bayesian Generalisation Error Bounds and Sparse Approximations. University of Edinburgh. [Bayes, Nonparametric, npbayes] pdf google scholar
David Heckerman. 2005. A Tutorial on Learning With Bayesian Networks, no MSR-TR-95-06. Microsoft Research. [Bayes, npbayes] pdf google scholar
Zoubin Ghahramani. 2005. Non-parametric Bayesian Methods. UAI '05 Tutorial. [Bayes, Nonparametric, npbayes] pdf google scholar
Zoubin Ghahramani. 2004. Bayesian Methods for Machine Learning. ICML '04 Tutorial. [Bayes, npbayes] pdf google scholar
Michael I. Jordan. 2004. Graphical Models. Statistical Science, vol 19, pp 140--155. [Bayes, npbayes] ps google scholar
D.M. Blei, T.L. Griffiths, M.I. Jordan and J.B. Tenenbaum. 2004. Hierarchical topic models and the nested Chinese restaurant process. In Advances in Neural Information Processing Systems 16: Proceedings of the 2003 Conference, pp 17. [Dirichlet, npbayes] pdf google scholar
Y. W. Teh , M. I. Jordan , M. J. Beal and D. M. Blei . 2004. Hierarchical Dirichlet Processes, no 653. Department of Statistics, University of California at Berkeley. [Dirichlet, npbayes] pdf google scholar
A. Ranganathan. 2004. The Dirichlet Process Mixture (DPM) Model. Internet draft, October 2006. [Dirichlet, npbayes] pdf google scholar
Matthias Seeger. 2004. Gaussian Processes for Machine Learning. [Bayes, Nonparametric, npbayes] pdf google scholar
S. Jain and R.M. Neal. 2004. A split-merge Markov chain Monte Carlo procedure for the Dirichlet process mixture model. Journal of Computational and Graphical Statistics, vol 13, no 1, pp 158--182. [Dirichlet, npbayes] pdf google scholar
Radford M. Neal. 2003. Introduction to Infinite Models. [Bayes, Nonparametric, npbayes] pdf google scholar
T.P. Minka. 2003. Estimating a Dirichlet distribution. Annals of Physics, vol 2000, no 8, pp 1--13. Technical report, MIT, 2000. [Dirichlet, npbayes] pdf url google scholar
David M. Blei, Andrew Y. Ng and Michael I. Jordan. 2003. Latent Dirichlet Allocation. Journal of Machine Learning Research. [Bayes, Dirichlet, npbayes, fulbright] url pdf google scholar
Matthias Seeger. 2002. Bayesian Gaussian Processes. [Bayes, Nonparametric, npbayes] pdf google scholar
Michael I. Jordan and Y. Weiss. 2002. Graphical models: Probabilistic inference. In The Handbook of Brain Theory and Neural Networks, Cambridge, MA. MIT Press. [Bayes, npbayes] ps google scholar
Kevin Murphy. 2002. Dynamic Bayesian Networks: Representation, Inference and Learning. UC Berkeley. PhD Thesis. [Bayes, npbayes] pdf google scholar
Matthias Seeger. 2002. Relationships between Gaussian processes, Support Vector machines and Smoothing Splines. [Bayes, Nonparametric, SVM, npbayes] pdf google scholar
Tony Jebara. 2002. Discriminative, Generative and Imitative Learning. MIT. [Bayes, npbayes] pdf google scholar
Jason Eisner. 2002. Discovering Syntactic Deep Structure via Bayesian Statistics. Cognitive Science. [Bayes, npbayes] pdf google scholar
Jason Eisner. 2002. Introduction to the Special Section on Linguistically Apt Statistical Methods. Cognitive Science. [Bayes, npbayes] pdf google scholar
Thomas P. Minka. 2001. Using lower bounds to approximate integrals. [Bayes, Variational, npbayes] pdf google scholar
Kevin Murphy. 2001. A Brief Introduction to Graphical Models. [Bayes, npbayes] pdf google scholar
C.E. Rasmussen. 2000. The infinite Gaussian mixture model. Advances in Neural Information Processing Systems, vol 12, pp 554--560. [Dirichlet, npbayes] pdf google scholar
R.M. Neal. 2000. Markov chain sampling methods for Dirichlet process mixture models. Journal of computational and graphical statistics, pp 249--265. [Dirichlet, npbayes] url google scholar
Giulio D'Agostini. 1999. Bayesian reasoning in high-energy physics: principles and applications. Yellow Report, no 99-03. CERN. [Bayes, npbayes] url google scholar
Matthias Seeger. 1999. Bayesian methods for Support Vector machines and Gaussian processes. [Bayes, Nonparametric, SVM, npbayes] pdf google scholar
Matthias Seeger. 1998. Bayesian Methods for Gaussian Processes, November. Seminar of Statistische Lerntheorie, Karlsruhe, Germany. [Bayes, Nonparametric, npbayes] ps google scholar
Kevin Murphy. 1998. A Brief Introduction to Graphical Models and Bayesian Networks. [Bayes, npbayes] url google scholar
G. Larry Bretthorst. 1996. An Introduction to Model Selection Using Probability Theory as Logic. In Maximum Entropy and Bayesian Methods. [Bayes, npbayes] pdf google scholar
M.D. Escobar and M. West. 1995. Bayesian density estimation and inference using mixtures. Journal of the american statistical association, pp 577--588. [Dirichlet, npbayes] url google scholar
R.E. Kass and A.E. Raftery. 1995. Bayes factors. Journal of the American Statistical Association, pp 773--795. [Dirichlet, npbayes] pdf google scholar
Radford M. Neal. 1993. Probabilistic inference using Markov chain Monte Carlo methods, no CRG-TR-93-1. Dept. of Computer Science, Univ. of Toronto. [Bayes, npbayes] pdf google scholar
R. M. Neal. 1991. Bayesian mixture modeling by Monte Carlo simulation. Technical Report, no CRG-TR-91-2. Dept. of Computer Science, University of Toronto. [Dirichlet, npbayes] pdf google scholar
G. Larry Bretthorst. 1990. An Introduction to Parameter Estimation Using Bayesian Probability Theory. In Maximum Entropy and Bayesian Methods. [Bayes, npbayes] pdf google scholar
Thomas S. Ferguson. 1973. A Bayesian Analysis of Some Nonparametric Problems. The Annals of Statistics. [Bayes, Nonparametric, npbayes] pdf google scholar
Peter Müller and Fernando A. Quintana. Nonparametric Bayesian Data Analysis. [Bayes, Nonparametric, npbayes] pdf google scholar
D. Spiegelhalter, A. Thomas, N. Best and W. Gilks. BUGS: Bayesian inference using Gibbs sampling. [Bayes, MCMC, npbayes] url google scholar
Matthias Seeger, et al. Efficient Nonparametric Bayesian Modelling with Sparse Gaussian Process Approximations. [Bayes, Nonparametric, npbayes] pdf google scholar
Thomas P. Minka. Variational Bounds via Reversing EM. [Bayes, Variational, npbayes] google scholar
David J. C. Mackay. Introduction to Gaussian Processes. [Bayes, Nonparametric, npbayes] pdf google scholar
Mike West. AST 383 Bayesian Statistics. [Bayes, npbayes] url google scholar
Tomi Jaakkola, Marina Meila and Tony Jebara. Maximum Entropy Discrimination. [Bayes, Maxent, npbayes] pdf google scholar
Tom Loredo. BIPS: Bayesian Inference for the Physical Sciences. [Bayes, npbayes] url google scholar

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