Graphical Models

Graphical Models

  • An HDP-HMM for systems with state persistence. E. Fox, E. Sudderth, M. I. Jordan, and A. Willsky. Proceedings of the 25th International Conference on Machine Learning (ICML), 2008.

  • The phylogenetic Indian buffet process: A non-exchangeable nonparametric prior for latent features. K. Miller, T. Griffiths and M. I. Jordan. In Uncertainty in Artificial Intelligence (UAI), Proceedings of the Twenty-Fourth Conference, 2008.

  • Hierarchical beta processes and the Indian buffet process. R. Thibaux, and M. I. Jordan. Technical Report 719, Department of Statistics, University of California, Berkeley, 2006.

  • Hierarchical Dirichlet processes. Y. W. Teh, M. I. Jordan, M. J. Beal and D. M. Blei. Journal of the American Statistical Association, 101, 1566-1581, 2006. [Software]

  • Log-determinant relaxation for approximate inference in discrete Markov random fields. M. J. Wainwright and M. I. Jordan. IEEE Transactions on Signal Processing, 54, 2099-2109, 2006.

  • The DLR hierarchy of approximate inference. M. Rosen-Zvi, M. I. Jordan, and A. Yuille. In Uncertainty in Artificial Intelligence (UAI), Proceedings of the Twenty-First 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.

  • Multiple-sequence functional annotation and the generalized hidden Markov phylogeny. J. D. McAuliffe, L. Pachter, and M. I. Jordan. Bioinformatics, 20, 1850-1860, 2004.

  • Learning graphical models for stationary time series. F. R. Bach and M. I. Jordan. IEEE Transactions on Signal Processing, 52, 2189-2199, 2004.

  • Kalman filtering with intermittent observations. B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, M. I. Jordan, and S. Sastry. In press: IEEE Transactions on Automatic Control, 2004.

  • Variational inference in graphical models: The view from the marginal polytope. M. J. Wainwright and M. I. Jordan. Forty-first Annual Allerton Conference on Communication, Control, and Computing, Urbana-Champaign, IL, 2004.

  • Hierarchical topic models and the nested Chinese restaurant process. D. M. Blei, T. Griffiths, M. I. Jordan, and J. Tenenbaum. In S. Thrun, L. Saul, and B. Schoelkopf (Eds.), Advances in Neural Information Processing Systems (NIPS) 16, 2004.

  • LOGOS: A modular Bayesian model for de novo motif detection. E. P. Xing, W. Wu, M. I. Jordan, and R. M. Karp. Journal of Bioinformatics and Computational Biology, 2, 127-154, 2004.

  • Graphical models, exponential families, and variational inference. M. J. Wainwright and M. I. Jordan. Technical Report 649, Department of Statistics, University of California, Berkeley, 2003.

  • Latent Dirichlet allocation. D. M. Blei, A. Y. Ng, and M. I. Jordan. Journal of Machine Learning Research, 3, 993-1022, 2003.

  • Beyond independent components: Trees and clusters. F. R. Bach and M. I. Jordan. Journal of Machine Learning Research, 4, 1205-1233, 2003. [Matlab code]

  • Modeling annotated data. D. M. Blei and M. I. Jordan. 26th International Conference on Research and Development in Information Retrieval (SIGIR), New York: ACM Press, 2003.

  • Hierarchical Bayesian models for applications in information retrieval. D. M. Blei, M. I. Jordan and A. Y. Ng. In: J. M. Bernardo, M. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith, and M. West (Eds.), Bayesian Statistics 7, 2003.

  • Kalman filtering with intermittent observations. B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, M. I. Jordan, and S. Sastry. In press: 42nd IEEE Conference on Decision and Control (CDC), 2004.

  • Learning graphical models with Mercer kernels. F. R. Bach and M. I. Jordan. In S. Becker, S. Thrun, and K. Obermayer (Eds.), Advances in Neural Information Processing Systems (NIPS) 15, 2003. and Blind Signal Separation (ICA), 2003.

  • A hierarchical Bayesian Markovian model for motifs in biopolymer sequences. E. P. Xing, M. I. Jordan, R. M. Karp and S. Russell. In S. Becker, S. Thrun, and K. Obermayer (Eds.), Advances in Neural Information Processing Systems (NIPS) 15, 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.

  • Tree-dependent component analysis. F. R. Bach and M. I. Jordan. In D. Koller and A. Darwiche (Eds)., Uncertainty in Artificial Intelligence (UAI), Proceedings of the Eighteenth Conference, 2002. [Matlab code]

  • Random sampling of a continuous-time stochastic dynamical system. M. Micheli and M. I. Jordan. Proceedings of the Fifteenth International Symposium on Mathematical Theory of Networks and Systems, 2002.

  • Thin junction trees. F. R. Bach and M. I. Jordan. In T. Dietterich, S. Becker and Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems (NIPS) 14, 2002.

  • Efficient stepwise selection in decomposable models. A. Deshpande, M. N. Garofalakis, and M. I. Jordan. In J. Breese and D. Koller (Ed)., Uncertainty in Artificial Intelligence (UAI), Proceedings of the Seventeenth Conference, 2001.

  • Learning with mixtures of trees. M. Meila and M. I. Jordan. Journal of Machine Learning Research, 1, 1-48, 2000.

  • Attractor dynamics for feedforward neural networks. L. K. Saul and M. I. Jordan. Neural Computation, 12, 1313-1335, 2000.

  • Loopy belief-propagation for approximate inference: An empirical study. K. Murphy, Y. Weiss, and M. I. Jordan. In K. B. Laskey and H. Prade (Eds.), Uncertainty in Artificial Intelligence (UAI), Proceedings of the Fifteenth Conference, San Mateo, CA: Morgan Kaufmann, 1999.

  • Variational probabilistic inference and the QMR-DT network. T. S. Jaakkola and M. I. Jordan. Journal of Artificial Intelligence Research, 10, 291-322, 1999.

  • 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.

  • Learning in graphical models. M. I. Jordan (Ed.), Cambridge MA: MIT Press, 1999.

  • Factorial hidden Markov models. Z. Ghahramani and M. I. Jordan. Machine Learning, 29, 245--273, 1997.

  • Optimal triangulation with continuous cost functions. M. Meila and M. I. Jordan. In M. C. Mozer, M. I. Jordan, and T. Petsche (Eds.), Advances in Neural Information Processing Systems (NIPS) 9, Cambridge MA: MIT Press, 1997.

  • Hidden Markov decision trees. M. I. Jordan, Z. Ghahramani, and L. K. Saul. In M. C. Mozer, M. I. Jordan, and T. Petsche (Eds.), Advances in Neural Information Processing Systems (NIPS) 9, Cambridge MA: MIT Press, 1997.

  • 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.

  • Markov mixtures of experts. M. Meila and M. I. Jordan. In Murray-Smith, R., and Johansen, T. A. (Eds.), Multiple Model Approaches to Modelling and Control, London: Taylor and Francis, 1997.

  • Mean field theory for sigmoid belief networks. L. K. Saul, T. Jaakkola, and M. I. Jordan. Journal of Artificial Intelligence Research, 4, 61-76, 1996.

  • Boltzmann chains and hidden Markov Models. L. K. Saul and M. I. Jordan. In G. Tesauro, D. S. Touretzky and T. K. Leen, (Eds.), Advances in Neural Information Processing Systems (NIPS) 7, MIT Press, 1995.

  • Learning in Boltzmann trees. L. K. Saul and M. I. Jordan. Neural Computation, 6, 1173-1183, 1994.