Part 1 Inference: introduction to inference for Bayesian networks, Robert Cowell; advanced inference in Bayesian networks, Robert Cowell; inference in Bayesian networks using nested junction trees, Uffe Kjoerulff; bucket elimination - a unifying framework for probabilistic inference, R. Dechter; an introduction to variational methods for graphical models, Michael I. Jordan et al; 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 walls in Markov chain Monte Carlo using ordered overrelaxation, Radford M. Neal. Part 2 Independence: chain graphs and symmetric associations, Thomas S. Richardson; the multiinformation function as a tool for measuring stochastic dependence, M. Studeny and J. Vejnarova. Part 3 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 4 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 et al; a hierarchical community of experts, Geoffrey E. Hinton et al; an information-theoretic analysis of hard and soft assignment methods for clustering, Michael J. Kearns et al; 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 study in MCMC, D.J. Spiegelhalter et al; prediction with Gaussian processes - from linear regression to linear prediction and beyond, C.K.I. Williams.
This book deals with an area that is central to modern statistical science and which has also attracted the interest of outstanding researchers beyond the statistical mainstream, from computer science, and neural computing. The book gives a vital and timely overview of current work at this interface, described by contributors representing the complete spectrum of backgrounds. -- Michael Titterington, Professor of Statistics, University of Glasgow Learning in Graphical Models is the product of a mutually exciting interaction between ideas, insights, and techniques drawn from the fields of statistics, computer science, and physics. With its authoritative tutorial papers and specialist articles by leading researchers, this collection provides an indispensable guide to a rapidly expanding subject. -- A.P. Dawid, Department of Statistical Science, University of College London The state of the art presented by the experts in the field. -- Ross D. Shachter, Department of Engineering-Economic Systems and Operations Research, Stanford University
Michael I. Jordan is Professor of Computer Science and of Statistics at the University of California, Berkeley, and recipient of the ACM/AAAI Allen Newell Award.
"The state of the art presented by the experts in the field." Ross D. Shachter , Department of Engineering-Economic Systemsand Operations Research, Stanford University