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Volker Tresp. 2006. Dirichlet Processes and Nonparametric Bayesian Modelling. [Bayes, Nonparametric, Dirichlet, npbayes] 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
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
Zoubin Ghahramani. 2005. Non-parametric Bayesian Methods. UAI '05 Tutorial. [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
Matthias Seeger. 2005. Bayesian Gaussian Process Models: PAC-Bayesian Generalisation Error Bounds and Sparse Approximations. University of Edinburgh. [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
Michael I. Jordan. 2004. Graphical Models. Statistical Science, vol 19, pp 140--155. [Bayes, npbayes] ps google scholar
Matthias Seeger. 2004. Gaussian Processes for Machine Learning. [Bayes, Nonparametric, npbayes] pdf google scholar
Zoubin Ghahramani. 2004. Bayesian Methods for Machine Learning. ICML '04 Tutorial. [Bayes, npbayes] pdf 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
Radford M. Neal. 2003. Introduction to Infinite Models. [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
Tony Jebara. 2002. Discriminative, Generative and Imitative Learning. MIT. [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
Kevin Murphy. 2002. Dynamic Bayesian Networks: Representation, Inference and Learning. UC Berkeley. PhD Thesis. [Bayes, npbayes] pdf google scholar
Jason Eisner. 2002. Discovering Syntactic Deep Structure via Bayesian Statistics. Cognitive Science. [Bayes, npbayes] pdf google scholar
Matthias Seeger. 2002. Bayesian Gaussian Processes. [Bayes, Nonparametric, npbayes] pdf google scholar
Matthias Seeger. 2002. Relationships between Gaussian processes, Support Vector machines and Smoothing Splines. [Bayes, Nonparametric, SVM, npbayes] pdf google scholar
Kevin Murphy. 2001. A Brief Introduction to Graphical Models. [Bayes, npbayes] pdf google scholar
Thomas P. Minka. 2001. Using lower bounds to approximate integrals. [Bayes, Variational, npbayes] pdf 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
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
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
D. Spiegelhalter, A. Thomas, N. Best and W. Gilks. BUGS: Bayesian inference using Gibbs sampling. [Bayes, MCMC, npbayes] url google scholar
Tomi Jaakkola, Marina Meila and Tony Jebara. Maximum Entropy Discrimination. [Bayes, Maxent, npbayes] pdf google scholar
David J. C. Mackay. Introduction to Gaussian Processes. [Bayes, Nonparametric, npbayes] pdf google scholar
Peter Müller and Fernando A. Quintana. Nonparametric Bayesian Data Analysis. [Bayes, Nonparametric, npbayes] pdf 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
Tom Loredo. BIPS: Bayesian Inference for the Physical Sciences. [Bayes, npbayes] url google scholar
Mike West. AST 383 Bayesian Statistics. [Bayes, npbayes] url google scholar

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