Learning in Graphical Models

Learning in Graphical Models

AngličtinaMäkká väzbaTlač na objednávku
Springer
EAN: 9789401061049
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Podrobné informácie

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.
EAN 9789401061049
ISBN 9401061041
Typ produktu Mäkká väzba
Vydavateľ Springer
Dátum vydania 29. októbra 2012
Stránky 630
Jazyk English
Rozmery 240 x 160
Krajina Netherlands
Čitatelia Professional & Scholarly
Ilustrácie XI, 630 p.
Editori Jordan, M.I.
Edícia Softcover reprint of the original 1st ed. 1998
Séria NATO Science Series D: