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

  • 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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