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Adaptive Computation & Machine Learning Ser:Learning Graphical Models +NEW NOTE
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Aproximadamente20,96 EUR
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“book may actually be new-unread-pages unturned other than bent corner NEW? LIKE NEW? VERY GOOD ? ”... Más informaciónacerca del estado
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Libro en perfecto estado y poco leído. La tapa no tiene desperfectos y si procede, con sobrecubierta para las tapas duras. Incluye todas las páginas sin arrugas ni roturas. El texto no está subrayado ni resaltado de forma alguna, y no hay anotaciones en los márgenes. Puede presentar marcas de identificación mínimas en la contraportada o las guardas. Muy poco usado. Consulta el anuncio del vendedor para obtener más información y la descripción de cualquier posible imperfección.
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Recogida:
Recogida local gratis en Portland, Oregon, Estados Unidos.
Envío:
Gratis USPS Media MailTM.
Ubicado en: Portland, Oregon, Estados Unidos
Entrega:
Entrega prevista entre el sáb. 2 ago. y el mié. 6 ago. a 94104
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N.º de artículo de eBay:395443919236
Características del artículo
- Estado
- Como nuevo
- Notas del vendedor
- Book Title
- Adaptive Computation & Machine Learning
- Narrative Type
- Nonfiction
- Subject
- Computers
- Intended Audience
- Adults
- Modified Item
- No
- Age Level
- Adults
- ISBN
- 9780262600323
Acerca de este producto
Product Identifiers
Publisher
MIT Press
ISBN-10
0262600323
ISBN-13
9780262600323
eBay Product ID (ePID)
590895
Product Key Features
Number of Pages
640 Pages
Language
English
Publication Name
Learning in Graphical Models
Publication Year
1999
Subject
Probability & Statistics / General, Intelligence (Ai) & Semantics
Type
Textbook
Subject Area
Mathematics, Computers
Series
Adaptive Computation and Machine Learning Ser.
Format
Trade Paperback
Dimensions
Item Height
1.4 in
Item Weight
41.8 Oz
Item Length
9.9 in
Item Width
7 in
Additional Product Features
Intended Audience
Scholarly & Professional
LCCN
98-038260
Reviews
"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
Dewey Edition
21
Grade From
College Graduate Student
Illustrated
Yes
Dewey Decimal
519.5
Synopsis
Graphical models, a marriage between probability theory and graph theory, provide a natural tool for dealing with two problems that occur throughout applied mathematics and engineering--uncertainty and complexity. In particular, they play an increasingly important role in the design and analysis of machine learning algorithms. Fundamental to the idea of a graphical model is the notion of modularity: a complex system is built by combining simpler parts. Probability theory serves as the glue whereby the parts are combined, ensuring that the system as a whole is consistent and providing ways to interface models to data. Graph theory provides both an intuitively appealing interface by which humans can model highly interacting sets of variables and a data structure that lends itself naturally to the design of efficient general-purpose algorithms. This book presents an in-depth exploration of issues related to learning within the graphical model formalism. Four chapters are tutorial chapters--Robert Cowell on Inference for Bayesian Networks, David MacKay on Monte Carlo Methods, Michael I. Jordan et al. on Variational Methods, and David Heckerman on Learning with Bayesian Networks. The remaining chapters cover a wide range of topics of current research interest.
LC Classification Number
QA279.L375 1999
Descripción del artículo del vendedor
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