Learning in Graphical Models (Adaptive Computation and Machine Learning series)

Learning in Graphical Models (Adaptive Computation and Machine Learning series)

By: Michael I. Jordan (editor), Francis Bach (series_editor)Paperback

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Graphical models, a marriage between probability theory and graph theory, provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering-uncertainty and complexity. In particular, they play an increasingly important role in the design and analysis of machine learning algorithms. Fundamental to the idea of a graphical model is the notion of modularity: a complex system is built by combining simpler parts. Probability theory serves as the glue whereby the parts are combined, ensuring that the system as a whole is consistent and providing ways to interface models to data. Graph theory provides both an intuitively appealing interface by which humans can model highly interacting sets of variables and a data structure that lends itself naturally to the design of efficient general-purpose algorithms. This book presents an in-depth exploration of issues related to learning within the graphical model formalism. Four chapters are tutorial chapters-Robert Cowell on Inference for Bayesian Networks, David MacKay on Monte Carlo Methods, Michael I. Jordan et al. on Variational Methods, and David Heckerman on Learning with Bayesian Networks. The remaining chapters cover a wide range of topics of current research interest.

About Author

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.


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.

Product Details

  • ISBN13: 9780262600323
  • Format: Paperback
  • Number Of Pages: 644
  • ID: 9780262600323
  • weight: 1093
  • ISBN10: 0262600323

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