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Probabilistic Graphical Models - 9780262013192
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Item specifics
- Condition
- Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
- Book Title
- Probabilistic Graphical Models
- ISBN
- 9780262013192
- Publication Name
- Probabilistic Graphical Models: Principles and Techniques
- Publisher
- MIT Press LTD
- Subject
- Computer Science, Mathematics
- Series
- Adaptive Computation and Machine Learning Series
- Publication Year
- 2009
- Type
- Textbook
- Format
- Hardcover
- Language
- English
- Item Height
- 229 mm
- Item Width
- 203 mm
- Item Weight
- 2132 g
- Number of Pages
- 1270 Pages
About this product
Product Information
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.
Product Identifiers
Publisher
MIT Press LTD
ISBN-13
9780262013192
eBay Product ID (ePID)
87851926
Product Key Features
Series
Adaptive Computation and Machine Learning Series
Publication Name
Probabilistic Graphical Models: Principles and Techniques
Format
Hardcover
Language
English
Subject
Computer Science, Mathematics
Publication Year
2009
Type
Textbook
Number of Pages
1270 Pages
Dimensions
Item Height
229 mm
Item Width
203 mm
Item Weight
2132 g
Additional Product Features
Country/Region of Manufacture
United States
Item description from the seller
Business seller information
Value Added Tax Number:
- GB 976952259
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