Learning in Graphical Models (NATO Science Series D:, 89) - Softcover

Softcover

ISBN 10: 9401061041 ISBN 13: 9789401061049

Publisher: Springer, 2012

Synopsis

In the past decade, a number of different research communities within the computational sciences have studied learning in networks, starting from a number of different points of view. There has been substantial progress in these different communities and surprising convergence has developed between the formalisms. The awareness of this convergence and the growing interest of researchers in understanding the essential unity of the subject underlies the current volume.
Two research communities which have used graphical or network formalisms to particular advantage are the belief network community and the neural network community. Belief networks arose within computer science and statistics and were developed with an emphasis on prior knowledge and exact probabilistic calculations. Neural networks arose within electrical engineering, physics and neuroscience and have emphasised pattern recognition and systems modelling problems. This volume draws together researchers from these two communities and presents both kinds of networks as instances of a general unified graphical formalism. The book focuses on probabilistic methods for learning and inference in graphical models, algorithm analysis and design, theory and applications. Exact methods, sampling methods and variational methods are discussed in detail.
Audience: A wide cross-section of computationally oriented researchers, including computer scientists, statisticians, electrical engineers, physicists and neuroscientists.

About this title

About the 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.

Review:

This book deals with an area that is central to modern statistical science and which has also attracted the interest of outstanding researchers beyond the statistical mainstream, from computer science, and neural computing. The book gives a vital and timely overview of current work at this interface, described by contributors representing the complete spectrum of backgrounds.

(Michael Titterington, Professor of Statistics, University of Glasgow)

Learning in Graphical Models is the product of a mutually exciting interaction between ideas, insights, and techniques drawn from the fields of statistics, computer science, and physics. With its authoritative tutorial papers and specialist articles by leading researchers, this collection provides an indispensable guide to a rapidly expanding subject.

(A.P. Dawid, Department of Statistical Science, University of College London)

The state of the art presented by the experts in the field.

(Ross D. Shachter, Department of Engineering-Economic Systems and Operations Research, Stanford University)

About this edition

• Publisher: Springer
• Publication date: 2012
• ISBN 10: 9401061041
• ISBN 13: 9789401061049
• Binding: Paperback
• Number of pages: 641
• Editor: Jordan M.I.