Learning in graphical models

TLDR: This paper presents an introduction to inference for Bayesian networks and a view of the EM algorithm that justifies incremental, sparse and other variants, as well as an information-theoretic analysis of hard and soft assignment methods for clustering.

Abstract: 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.