Learning in Graphical Models
Language: English
Published by Springer, Springer Okt 2012, 2012
- Softcover
- New
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This item is printed on demand - Print on Demand Titel. Neuware -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.Springer-Verlag KG, Sachsenplatz 4-6, 1201 Wien 648 pp. Englisch.…
Seller Inventory # 9789401061049
- Title
- Learning in Graphical Models
- Author
- M. I. Jordan
- Publisher
- Springer, Springer Okt 2012
- Publication year
- 2012
- Condition
- Neu
- Binding
- Taschenbuch
- Language
- English
- ISBN 10
- 9401061041
- ISBN 13
- 9789401061049
- Item weight
- 1,019 grams
- Dimensions
- 240x160x35 mm
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.
"Synopsis" may belong to another edition of this title.
Review
& quot; The state of the art presented by the experts in the field.& quot; -- Ross D. Shachter, Department of Engineering-Economic Systems and Operations Research, Stanford University
"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 the title" may belong to another edition of this title.
buchversandmimpf2000
Emtmannsberg, BAYE, Germany
AbeBooks seller since January 23, 2017
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