1. Book: Learning in Graphical Models
The following is a book which readers of this list might find of
interest. For more information please visit
http://mitpress.mit.edu/promotions/books/JORLPS99
LEARNING IN GRAPHICAL MODELS
edited by Michael I. Jordan
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 explores 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 range
of topics of current research interest.
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
2. Nationwide ISPs support VPDN
3. CFP: Learning Graphical Models for Comp Genomics (Extended)
4. Q: Which W98 driver to use for printer DEC-laser 1152 in PCL4-mode?
5. CFP: Learning Graphical Models for Computational Genomics
6. Apple Studio Display and Quadra 800
7. 2nd CFP: Workshop on Learning Graphical Models for Computational Genomics
8. Remote Control\Node program for OS/2
9. new book on graphical models
10. CfP: Machine Learning for User Modeling WS at UM99
11. Postdoctoral Position: Applying Machine Learning to Ecosystem Modeling
12. Familiarization Workshop Knowledge Level Models of Machine Learning
by