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    Artikelmerkmale

    Artikelzustand
    Gut: Buch, das gelesen wurde, sich aber in einem guten Zustand befindet. Der Einband weist nur sehr ...
    Release Year
    2009
    Book Title
    Probabilistic Graphical Models: Principles and Techniques (Ada...
    ISBN
    9780262013192
    Kategorie

    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
    Author
    Daphne Koller
    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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