Learning in Graphical Models

Preface; M.I. Jordan. Part I: Inference. Introduction to Inference for Bayesian Networks; R. Cowell. Advanced Inference in Bayesian Networks; R. Cowell. Inference in Bayesian Networks Using Nested Junction Trees; U. Kjærulff. Bucket Elimination: A Unifying Framework for Probabilistic Inference; R. Dechter. An Introduction to Variational Methods for Graphical Models; M.I. Jordan, et al. Improving the Mean Field Approximation via the Use of Mixture Distributions; T.S. Jaakkola, M.I. Jordan. Introduction to Monte Carlo Methods; D.J.C. MacKay. Suppressing Random Walks in Markov Chain Monte Carlo Using Ordered Overrelaxation; R.M. Neal. Part II: Independence. Chain Graphs and Symmetric Associations; T.S. Richardson. The Multiinformation Function as a Tool for Measuring Stochastic Dependence; M. Studený, J. Vejnarová. Part III: Foundations for Learning. A Tutorial on Learning with Bayesian Networks; D. Heckerman. A View of the EM Algorithm that Justifies Incremental, Sparse, and Other Variants; R.M. Neal, G.E. Hinton. Part IV: Learning from Data. Latent Variable Models; C.M. Bishop. Stochastic Algorithms for Exploratory Data Analysis: Data Clustering and Data Visualization; J.M. Buhmann. Learning Bayesian Networks with Local Structure; N. Friedman, M. Goldszmidt. Asymptotic Model Selection for Directed Networks with Hidden Variables; D. Geiger, et al. A Hierarchical Community of Experts; G.E. Hinton, et al. An Information-Theoretic Analysis of Hard and Soft Assignment Methods for Clustering; M.J. Kearns, et al. Learning Hybrid Bayesian Networks from Data; S. Monti, G.F. Cooper. A Mean Field Learning Algorithm for UnsupervisedNatural Networks; L. Saul, M.I. Jordan. Edge Exclusion Tests for Graphical Gaussian Models; P.W.F. Smith, J. Whittaker. Hepatitis B: A Case Study in MCMC; D.J. Spiegelhalter, et al. Prediction with Gaussian Processes: From Linear Regression to Linear Prediction and Beyond; C.K.I. Williams. Subject Index.