Learning in Graphical Models

[Michael Jordan]

The following book is available from MIT Press; see

http://mitpress.mit.edu/promotions/books/JORLPS99


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


Adaptive Computation and Machine Learning series

7 x 10, 648 pp.

paper ISBN 0-262-60032-3