Picture 1 of 1
Stock photo
Picture 1 of 1
Stock photo
Adaptive Computation and Machine Learning Ser.: Probabilistic Graphical Models : Principles and Techniques by Daphne Koller (2009, Hardcover)
A
avr9610 (16)
86.4% positive feedback
Price:
$99.90
Free shipping
Returns:
Condition:
The product is a textbook titled "Probabilistic Graphical Models: Principles and Techniques," part of the Adaptive Computation and Machine Learning Series published by MIT Press in 2009. Authored by Nir Friedman and Daphne Koller, the book covers subjects in Computers, Mathematics, Programming, Algorithms, Artificial Intelligence, Bayesian Analysis, and Probability & Statistics. With a comprehensive 1270 pages and a substantial size of 9.4 inches in length and 8.3 inches in width, this hardcover book is a valuable resource for students and professionals interested in the fields of AI, machine learning, and graphical models.
- Buy It NowAdaptive Computation and Machine Learning Ser.: Probabilistic Graphical Models :
Oops! Looks like we're having trouble connecting to our server.
Refresh your browser window to try again.
About this product
Product Identifiers
PublisherMIT Press
ISBN-100262013193
ISBN-139780262013192
eBay Product ID (ePID)73169822
Product Key Features
Number of Pages1270 Pages
Publication NameProbabilistic Graphical Models : Principles and Techniques
LanguageEnglish
Publication Year2009
SubjectProgramming / Algorithms, Intelligence (Ai) & Semantics, Probability & Statistics / Bayesian Analysis
TypeTextbook
Subject AreaMathematics, Computers
AuthorDaphne Koller
SeriesAdaptive Computation and Machine Learning Ser.
FormatHardcover
Dimensions
Item Height2 in
Item Weight78 Oz
Item Length9.4 in
Item Width8.3 in
Additional Product Features
Intended AudienceTrade
LCCN2009-008615
Dewey Edition22
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
IllustratedYes
Dewey Decimal519.5/420285
SynopsisA 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 NumberQA279.5.K65 2010
All listings for this product
Ratings and Reviews
Most relevant reviews
- Aug 02, 2021
Excellent summary of research and practice
Very thorough.Verified purchase: YesCondition: Pre-owned
