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

ISBN 10: 0792350170 / ISBN 13: 9780792350170

Verlag: Springer, 1998

Beschreibung

Supports Goodwill of Silicon Valley job training programs. The cover and pages are in very good condition! The cover and any other included accessories are also in very good condition showing some minor use. The spine is straight, there are no rips tears or creases on the cover or the pages.

Bestandsnummer des Verkäufers GWSVV.0792350170.VG

Bibliografische Details

• Titel: Learning in Graphical Models (NATO Science Series D:, 89)
• Verlag: Springer
• Erscheinungsjahr: 1998
• Sprache: Englisch
• ISBN-10: 0792350170
• ISBN-13: 9780792350170
• Einband: Hardcover
• Zustand: very_good

Über diesen Titel

Inhaltsangabe

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.