Tutorials and Reviews

Tutorials and Reviews

  • Bayesian nonparametric learning: Expressive priors for intelligent systems. M. I. Jordan. In R. Dechter, H. Geffner, and J. Halpern (Eds.), Heuristics, Probability and Causality: A Tribute to Judea Pearl, College Publications, 2010.

  • Hierarchical models, nested models and completely random measures. M. I. Jordan. In M.-H. Chen, D. Dey, P. Mueller, D. Sun, and K. Ye (Eds.), Frontiers of Statistical Decision Making and Bayesian Analysis: In Honor of James O. Berger, New York: Springer, 2010.

  • Hierarchical Bayesian nonparametric models with applications. Y. W. Teh and M. I. Jordan. In N. Hjort, C. Holmes, P. Mueller, and S. Walker (Eds.), Bayesian Nonparametrics: Principles and Practice, Cambridge, UK: Cambridge University Press, 2010.

  • Graphical models, exponential families, and variational inference. M. J. Wainwright and M. I. Jordan. Foundations and Trends in Machine Learning, 1, 1-305, 2008. [Substantially revised and expanded version of a 2003 technical report.]

  • Dirichlet processes, Chinese restaurant processes and all that. M. I. Jordan. Tutorial presentation at the NIPS Conference, 2005.

  • A variational principle for graphical models. M. J. Wainwright and M. I. Jordan. New Directions in Statistical Signal Processing: From Systems to Brain. Cambridge, MA: MIT Press, 2005.

  • Graphical models. M. I. Jordan. Statistical Science (Special Issue on Bayesian Statistics), 19, 140-155, 2004.

  • An introduction to MCMC for machine learning. C. Andrieu, N. de Freitas, A. Doucet and M. I. Jordan. Machine Learning, 50, 5-43, 2003.

  • Graphical models: Probabilistic inference. M. I. Jordan and Y. Weiss. In M. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks, 2nd edition. Cambridge, MA: MIT Press, 2002.

  • Learning in modular and hierarchical systems. M. I. Jordan and R. A. Jacobs. In M. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks, 2nd edition. Cambridge, MA: MIT Press, 2002.

  • Discorsi sulle reti neurali e l'apprendimento. C. Domeniconi and M. I. Jordan. Milan: Edizioni Franco Angeli, 2001.

  • An introduction to variational methods for graphical models. M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul. In M. I. Jordan (Ed.), Learning in Graphical Models, Cambridge: MIT Press, 1999.

  • Computational motor control. M. I. Jordan and D. M. Wolpert. In M. Gazzaniga (Ed.), The Cognitive Neurosciences, 2nd edition, Cambridge: MIT Press, 1999.

  • Neural networks. M. I. Jordan and C. Bishop. In Tucker, A. B. (Ed.), CRC Handbook of Computer Science, Boca Raton, FL: CRC Press, 1997.

  • Probabilistic independence networks for hidden Markov probability models. P. Smyth, D. Heckerman, and M. I. Jordan. Neural Computation, 9, 227-270, 1997.

  • Computational aspects of motor control and motor learning. M. I. Jordan. In H. Heuer and S. Keele (Eds.), Handbook of Perception and Action: Motor Skills, New York: Academic Press, 1996.

  • Why the logistic function? A tutorial discussion on probabilities and neural networks. M. I. Jordan. MIT Computational Cognitive Science Report 9503, August 1995.

  • Optimal control: A foundation for intelligent control. D. A. White and M. I. Jordan. In D. A. White, and D. A. Sofge (Eds.), Handbook of Intelligent Control, Amsterdam: Van Nostrand, 1992.

  • An introduction to linear algebra in parallel, distributed processing. M. I. Jordan. In D. E. Rumelhart and J. L. McClelland, (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Cambridge, MA: MIT Press, 1986.