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eBay-Artikelnr.:333717965527
Artikelmerkmale
- Artikelzustand
- Title
- Probabilistic Graphical Models: Principles and Techniques (Adapt
- Artist
- Not Specified
- ISBN
- 9780262013192
Über dieses Produkt
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
Publication Year
2009
Subject
Programming / Algorithms, Intelligence (Ai) & Semantics, Probability & Statistics / Bayesian Analysis
Type
Textbook
Subject Area
Mathematics, Computers
Series
Adaptive Computation and Machine Learning Ser.
Format
Hardcover
Dimensions
Item Height
2 in
Item Weight
78 Oz
Item Length
9.4 in
Item Width
8.3 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
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., 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
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Verkäuferbewertungen (556'847)
- g***r (123)- Bewertung vom Käufer.Letzter MonatBestätigter KaufGreat deal. So smooth and on time. Thank you
- i***. (37)- Bewertung vom Käufer.Letzter MonatBestätigter KaufWell packed, book as described. Thanks.
- p***p (36)- Bewertung vom Käufer.Letzter MonatBestätigter KaufI originally purchased this hard to find book via the Awesome Books USA website, and after two weeks it still hadn’t shipped. Took a chance on the EBay listing and boom, shipped right away.