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Innovations in Bayesian Networks: Theory and Applications: 156
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Innovations in Bayesian Networks: Theory and Applications: 156 - Pasta blanda

2010, ISBN: 9783642098758

redacteur: Holmes, Dawn E. Springer, Paperback, Auflage: Softcover reprint of hardcover 1st ed. 2008, 332 Seiten, Publiziert: 2010-11-30T00:00:01Z, Produktgruppe: Book, Hersteller-Nr.: bl… Más…

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Innovations in Bayesian Networks Theory and Applications - Holmes, Dawn E. (Herausgeber)
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Holmes, Dawn E. (Herausgeber):

Innovations in Bayesian Networks Theory and Applications - Pasta blanda

2010, ISBN: 3642098754

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Softcover reprint of hardcover 1st ed. 2008 Kartoniert / Broschiert Künstliche Intelligenz, bayesiannetworks; Bayesiannetwork; LearningBayesiannetworks; Topologie; ArtificialIntelligenc… Más…

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Innovations in Bayesian Networks: Theory and Applications (Studies in Computational Intelligence (156)) - Pasta blanda

2010

ISBN: 3642098754

[EAN: 9783642098758], Tweedehands, als nieuw, [SC: 35.04], [PU: Springer], Like New, Books

NOT NEW BOOK. Gastos de envío: EUR 35.04 Mispah books, Redhill, SURRE, United Kingdom [82663586] [Beoordeling: 4 (van 5)]
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Innovations in Bayesian Networks: Theory and Applications (Studies in Computational Intelligence (156)) - Pasta blanda

2010, ISBN: 3642098754

[EAN: 9783642098758], D'occasion, comme neuf, [SC: 23.11], [PU: Springer], Like New, Books

NOT NEW BOOK. Gastos de envío: EUR 23.11 Mispah books, Redhill, SURRE, United Kingdom [82663586] [Note: 4 (sur 5)]
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Innovations in Bayesian Networks : Theory and Applications - Holmes, Dawn E.
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Holmes, Dawn E.:
Innovations in Bayesian Networks : Theory and Applications - Pasta blanda

ISBN: 9783642098758

paperback, [PU: Springer, Berlin/Heidelberg/New York, NY]

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Detalles del libro
Innovations in Bayesian Networks: Theory and Applications: 156

Bayesian networks currently provide one of the most rapidly growing areas of research in computer science and statistics. In compiling this volume we have brought together contributions from some of the most prestigious researchers in this field. Each of the twelve chapters is self-contained.Both theoreticians and application scientists/engineers in the broad area of artificial intelligence will find this volume valuable. It also provides a useful sourcebook for Graduate students since it shows the direction of current research.

Detalles del libro - Innovations in Bayesian Networks: Theory and Applications: 156


EAN (ISBN-13): 9783642098758
ISBN (ISBN-10): 3642098754
Tapa dura
Tapa blanda
Año de publicación: 2010
Editorial: Springer
332 Páginas
Peso: 0,503 kg
Idioma: eng/Englisch

Libro en la base de datos desde 2010-10-29T03:02:47-05:00 (Mexico City)
Página de detalles modificada por última vez el 2024-03-16T03:47:01-06:00 (Mexico City)
ISBN/EAN: 9783642098758

ISBN - escritura alterna:
3-642-09875-4, 978-3-642-09875-8
Mode alterno de escritura y términos de búsqueda relacionados:
Autor del libro: holmes, holme
Título del libro: innovations bayesian networks, innovation networks, bayes networks


Datos del la editorial

Autor: Dawn E. Holmes
Título: Studies in Computational Intelligence; Innovations in Bayesian Networks - Theory and Applications
Editorial: Springer; Springer Berlin
322 Páginas
Año de publicación: 2010-11-30
Berlin; Heidelberg; DE
Impreso en
Idioma: Inglés
160,49 € (DE)
164,99 € (AT)
177,00 CHF (CH)
POD
X, 322 p. 92 illus.

BC; Hardcover, Softcover / Technik/Allgemeines, Lexika; Mathematik für Ingenieure; Verstehen; Bayesian Networks; Bayesian network; Learning Bayesian networks; Topologie; artificial intelligence; computational intelligence; intelligence; learning; modeling; statistics; system modeling; Mathematical and Computational Engineering Applications; Artificial Intelligence; Künstliche Intelligenz; BB

to Bayesian Networks.- A Polemic for Bayesian Statistics.- A Tutorial on Learning with Bayesian Networks.- The Causal Interpretation of Bayesian Networks.- An Introduction to Bayesian Networks and Their Contemporary Applications.- Objective Bayesian Nets for Systems Modelling and Prognosis in Breast Cancer.- Modeling the Temporal Trend of the Daily Severity of an Outbreak Using Bayesian Networks.- An Information-Geometric Approach to Learning Bayesian Network Topologies from Data.- Causal Graphical Models with Latent Variables: Learning and Inference.- Use of Explanation Trees to Describe the State Space of a Probabilistic-Based Abduction Problem.- Toward a Generalized Bayesian Network.- A Survey of First-Order Probabilistic Models.
Presents the innovative paradigms related to the theory and practical applications of Bayesian Networks

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