内容

目次

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, MichaelI. Jordan et al; improving the mean field approximation via the use ofmixture distributions, Tommi S. Jaakkola and Michael I. Jordan; introductionto Monte Carlo methods, D.J.C. MacKay; suppressing random walls in Markovchain Monte Carlo using ordered overrelaxation, Radford M. Neal. Part 2Independence: 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 onlearning with Bayesian networks, David Heckerman; a view of the EM algorithmthat justifies incremental, sparse and other variants, Radford M. Neal andGeoffrey 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 Bayesiannetworks with local structure, Nir Friedman and Moises Goldszmidt; asymptoticmodel selection for directed networks with hidden variables, Dan Geiger etal; a hierarchical community of experts, Geoffrey E. Hinton et al; aninformation-theoretic analysis of hard and soft assignment methods forclustering, Michael J. Kearns et al; learning hybrid Bayesian networks fromdata, Stefano Monti and Gregory F. Cooper; a mean field learning algorithmfor unsupervised neural networks, Lawrence Saul and Michael Jordan; edgeexclusion tests for graphical Gaussian models, Peter W.F. Smith and JoeWhittaker; hepatitis B - a case study in MCMC, D.J. Spiegelhalter et al;prediction with Gaussian processes - from linear regression to linearprediction and beyond, C.K.I. Williams.