Learning in Graphical Models

Papers from Jordan's collection of articles Learning in Graphical Models .
Warning: these versions may be slightly different from those appeared in the book.

Part I Inference

Inference in Bayesian Networks using Nested Junction Trees Uffe Kjaerulff Bucket Elimination: A Unifying Framework for Probabilistic Inference Rina Dechter An Introduction to Variational Methods for Graphical Models Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, L.K. Saul Improving the Mean Field Approximation Via the Use of Mixture Distributions Tommi S. Jaakkola and Michael I. Jordan Introduction to Monte Carlo Methods D. J. C. MacKay Suppressing Random Walks in Markov Chain Monte Carlo using Ordered Overrelaxation Radford M. Neal
Bucket Elimination: A Unifying Framework for Probabilistic Inference Rina Dechter An Introduction to Variational Methods for Graphical Models Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, L.K. Saul Improving the Mean Field Approximation Via the Use of Mixture Distributions Tommi S. Jaakkola and Michael I. Jordan Introduction to Monte Carlo Methods D. J. C. MacKay Suppressing Random Walks in Markov Chain Monte Carlo using Ordered Overrelaxation Radford M. Neal

Part II Independence

Chain Graphs and Symmetric Associations Thomas S. Richardson The Multi-information Function as a Tool for Measuring Stochastic Dependence M. Studeny and J. Vejnarová;

Part III Foundations for Learning

A Tutorial on Learning with Bayesian Networks David Heckerman A View of the EM Algorithm that Justifies Incremental, Sparse, and Other Variants Radford M. Neal and Geoffrey E. Hinton

Part IV Learning From Data

Latent Variable Models Christopher M. Bishop Stochastic Algorithms for Exploratory Data Analysis: Data Clustering and Data Visualization Joachim M. Buhmann Learning Bayesian Networks with Local Structure Nir Friedman and Moises Goldszmidt Asymptotic Model Selection for Directed Networks with Hidden Variables Dan Geiger, David Heckerman, and Christopher Meek A Hierarchical Community of Experts Geoffrey E. Hinton, Brian Sallans, and Zoubin Ghahramani An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering Michael J. Kearns, Yishay Mansour, and A ndrew Y. Ng Learning Hybrid Bayesian Networks from Data Stefano Monti and Gregory F. Cooper A Mean Field Learning Algorithm for Unsupervised Neural Networks Lawrence Saul and Michael Jordan Edge Exclusion Tests for Graphical Gaussian Models Peter W. F. Smith and Joe Whittaker Hepatitis B: A Case Sudy in MCMC D. J. Spiegelhalter, N. G. Best, W. R. Gilks, and H. Inskip Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond C. K. I. Williams

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