Learning in Graphical Models

1. Front Matter

   Pages i-5

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2. Inference

   1. Front Matter

      Pages 7-7

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   2. Introduction to Inference for Bayesian Networks

      • Robert Cowell

      Pages 9-26

   3. Advanced Inference in Bayesian Networks

      • Robert Cowell

      Pages 27-49

   4. Inference in Bayesian Networks Using Nested Junction Trees

      • Uffe Kjærulff

      Pages 51-74

   5. Bucket Elimination: A Unifying Framework for Probabilistic Inference

      • R. Dechter

      Pages 75-104

   6. An Introduction to Variational Methods for Graphical Models

      • Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, Lawrence K. Saul

      Pages 105-161

   7. Improving the Mean Field Approximation Via the Use of Mixture Distributions

      • Tommi S. Jaakkola, Michael I. Jordan

      Pages 163-173

   8. Introduction to Monte Carlo Methods

      • D. J. C. Mackay

      Pages 175-204

   9. Suppressing Random Walks in Markov Chain Monte Carlo Using Ordered Overrelaxation

      • Radford M. Neal

      Pages 205-228

3. Independence

   1. Front Matter

      Pages 229-229

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   2. Chain Graphs and Symmetric Associations

      • Thomas S. Richardson

      Pages 231-259

   3. The Multiinformation Function as a Tool for Measuring Stochastic Dependence

      • M. Studený, J. Vejnarová

      Pages 261-297

4. Foundations for Learning

   1. Front Matter

      Pages 299-299

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   2. A Tutorial on Learning with Bayesian Networks

      • David Heckerman

      Pages 301-354

   3. A View of the Em Algorithm that Justifies Incremental, Sparse, and other Variants

      • Radford M. Neal, Geoffrey E. Hinton

      Pages 355-368

5. Learning from Data

   1. Front Matter

      Pages 369-369

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   2. Latent Variable Models

      • Christopher M. Bishop

      Pages 371-403

   3. Stochastic Algorithms for Exploratory Data Analysis: Data Clustering and Data Visualization

      • Joachim M. Buhmann

      Pages 405-419

   4. Learning Bayesian Networks with Local Structure

      • Nir Friedman, Moises Goldszmidt

      Pages 421-459

In the past decade, a number of different research communities within the computational sciences have studied learning in networks, starting from a number of different points of view. There has been substantial progress in these different communities and surprising convergence has developed between the formalisms. The awareness of this convergence and the growing interest of researchers in understanding the essential unity of the subject underlies the current volume.
Two research communities which have used graphical or network formalisms to particular advantage are the belief network community and the neural network community. Belief networks arose within computer science and statistics and were developed with an emphasis on prior knowledge and exact probabilistic calculations. Neural networks arose within electrical engineering, physics and neuroscience and have emphasised pattern recognition and systems modelling problems. This volume draws together researchers from these two communities and presents both kinds of networks as instances of a general unified graphical formalism. The book focuses on probabilistic methods for learning and inference in graphical models, algorithm analysis and design, theory and applications. Exact methods, sampling methods and variational methods are discussed in detail.
Audience: A wide cross-section of computationally oriented researchers, including computer scientists, statisticians, electrical engineers, physicists and neuroscientists.

• Bayesian network
• Latent variable model
• Monte Carlo method
• algorithms
• clustering
• data analysis
• electrical engineering
• expectation–maximization algorithm
• learning
• linear regression
• proving
• visualization

• Massachusetts Institute of Technology, Cambridge, USA

  Michael I. Jordan

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