Date: Tue, 12 Jan 1999 09:18:19 -0800 (PST) From: Michael Jordan jordan@CS.Berkeley.EDU Subject: Learning in Graphical Models Web: http://mitpress.mit.edu/promotions/books/JORLPS99 The following book is available from MIT Press; see above LEARNING IN GRAPHICAL MODELS Michael I. Jordan, Ed. 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. PART I: INFERENCE Robert G. Cowell Uffe Kjaerulff Rina Dechter Michael I. Jordan, Zoubin Ghahramani, Tommi S. Jaakkola, and Lawrence K. Saul Tommi S. Jaakkola and Michael I. Jordan David J. C. MacKay Radford M. Neal PART II: INDEPENDENCE Thomas S. Richardson Milan Studeny and Jirina Vejnarova PART III: FOUNDATIONS FOR LEARNING David Heckerman Radford M. Neal and Geoffrey E. Hinton PART IV: LEARNING FROM DATA Christopher M. Bishop Joachim M. Buhmann Nir Friedman and Moises Goldszmidt Dan Geiger, David Heckerman, and Christopher Meek Geoffrey E. Hinton, Brian Sallans, and Zoubin Ghahramani Michael J. Kearns, Yishay Mansour, and Andrew Y. Ng Stefano Monti and Gregory F. Cooper Lawrence K. Saul and Michael I. Jordan Peter W. F. Smith and Joe Whittaker David J. Spiegelhalter, Nicky G. Best, Wally R. Gilks, and Hazel Inskip Christopher K. I. Williams
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