Warning: these versions may be slightly different from those appeared in the book.

- 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*- Inference in Bayesian Networks using Nested Junction Trees
- Chain Graphs and Symmetric Associations

*Thomas S. Richardson*- The Multi-information Function as a Tool for Measuring Stochastic Dependence

*M. Studeny and J. Vejnarová;*- Chain Graphs and Symmetric Associations
- 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*- Latent Variable Models

**Part II Independence**

**Part III Foundations for Learning**

**Part IV Learning From Data**

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