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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.
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Review
" 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
& 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 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.
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Taschenbuch. Condition: Neu. This item is printed on demand - it takes 3-4 days longer - 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. 648 pp. Englisch. Seller Inventory # 9789401061049
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Taschenbuch. Condition: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Seller Inventory # 9789401061049
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Taschenbuch. Condition: Neu. 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 GmbH, Tiergartenstr. 17, 69121 Heidelberg 648 pp. Englisch. Seller Inventory # 9789401061049
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Paperback. Condition: new. Paperback. 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. 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. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9789401061049
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Condition: New. Proceedings of the NATO Advanced Study Institute, Ettore Maiorana Centre, Erice, Italy, September 27-October 7, 1996 Editor(s): Jordan, Michael I. Series: NATO Science Series D:. Num Pages: 641 pages, biography. BIC Classification: PBT; PHS; UYQ. Category: (P) Professional & Vocational. Dimension: 237 x 158 x 40. Weight in Grams: 960. . 2012. Softcover reprint of the original 1st ed. 1998. Paperback. . . . . Seller Inventory # V9789401061049
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Condition: New. Proceedings of the NATO Advanced Study Institute, Ettore Maiorana Centre, Erice, Italy, September 27-October 7, 1996 Editor(s): Jordan, Michael I. Series: NATO Science Series D:. Num Pages: 641 pages, biography. BIC Classification: PBT; PHS; UYQ. Category: (P) Professional & Vocational. Dimension: 237 x 158 x 40. Weight in Grams: 960. . 2012. Softcover reprint of the original 1st ed. 1998. Paperback. . . . . Books ship from the US and Ireland. Seller Inventory # V9789401061049
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