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Probabilistic Graphical Models : Principles and Techniques, Hardcover by Koll...
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N.º de artículo de eBay:364832963328
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Características del artículo

Estado
Nuevo: Libro nuevo, sin usar y sin leer, que está en perfecto estado; incluye todas las páginas sin ...
Book Title
Probabilistic Graphical Models : Principles and Techniques
ISBN
9780262013192
Subject Area
Mathematics, Computers
Publication Name
Probabilistic Graphical Models : Principles and Techniques
Publisher
MIT Press
Item Length
9.2 in
Subject
Programming / Algorithms, Intelligence (Ai) & Semantics, Probability & Statistics / Bayesian Analysis
Publication Year
2009
Series
Adaptive Computation and Machine Learning Ser.
Type
Textbook
Format
Hardcover
Language
English
Item Height
2 in
Author
Nir Friedman, Daphne Koller
Item Weight
74.1 Oz
Item Width
8.2 in
Number of Pages
1270 Pages

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Product Identifiers

Publisher
MIT Press
ISBN-10
0262013193
ISBN-13
9780262013192
eBay Product ID (ePID)
73169822

Product Key Features

Number of Pages
1270 Pages
Publication Name
Probabilistic Graphical Models : Principles and Techniques
Language
English
Subject
Programming / Algorithms, Intelligence (Ai) & Semantics, Probability & Statistics / Bayesian Analysis
Publication Year
2009
Type
Textbook
Author
Nir Friedman, Daphne Koller
Subject Area
Mathematics, Computers
Series
Adaptive Computation and Machine Learning Ser.
Format
Hardcover

Dimensions

Item Height
2 in
Item Weight
74.1 Oz
Item Length
9.2 in
Item Width
8.2 in

Additional Product Features

Intended Audience
Trade
LCCN
2009-008615
Dewey Edition
22
Reviews
"This landmark book provides a very extensive coverage of the field, ranging from basic representational issues to the latest techniques for approximate inference and learning. As such, it is likely to become a definitive reference for all those who work in this area. Detailed worked examples and case studies also make the book accessible to students." -Kevin Murphy, Department of Computer Science, University of British Columbia
CLASSIFICATION_METADATA
{"IsNonfiction":["No"],"IsOther":["No"],"IsAdult":["No"],"MuzeFormatDesc":["Hardcover"],"IsChildren":["No"],"Genre":["MATHEMATICS","COMPUTERS"],"Topic":["Programming / Algorithms","Probability & Statistics / Bayesian Analysis","Intelligence (AI) & Semantics"],"IsTextBook":["Yes"],"IsFiction":["No"]}
Illustrated
Yes
Dewey Decimal
519.5/420285
Synopsis
A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions., A general framework for constructing and using probabilistic models of complex systems that would enable a computer to use available information for making decisions. Most tasks require a person or an automated system to reason--to reach conclusions based on available information. The framework of probabilistic graphical models, presented in this book, provides a general approach for this task. The approach is model-based, allowing interpretable models to be constructed and then manipulated by reasoning algorithms. These models can also be learned automatically from data, allowing the approach to be used in cases where manually constructing a model is difficult or even impossible. Because uncertainty is an inescapable aspect of most real-world applications, the book focuses on probabilistic models, which make the uncertainty explicit and provide models that are more faithful to reality. Probabilistic Graphical Models discusses a variety of models, spanning Bayesian networks, undirected Markov networks, discrete and continuous models, and extensions to deal with dynamical systems and relational data. For each class of models, the text describes the three fundamental cornerstones: representation, inference, and learning, presenting both basic concepts and advanced techniques. Finally, the book considers the use of the proposed framework for causal reasoning and decision making under uncertainty. The main text in each chapter provides the detailed technical development of the key ideas. Most chapters also include boxes with additional material: skill boxes, which describe techniques; case study boxes, which discuss empirical cases related to the approach described in the text, including applications in computer vision, robotics, natural language understanding, and computational biology; and concept boxes, which present significant concepts drawn from the material in the chapter. Instructors (and readers) can group chapters in various combinations, from core topics to more technically advanced material, to suit their particular needs.
LC Classification Number
QA279.5.K65 2010
Copyright Date
2009
ebay_catalog_id
4

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