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

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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