Probabilistic Graphical ModelsPrinciples and Techniques 非扫描清晰版本

上传者: lion003 | 上传时间: 2021-12-04 01:39:17 | 文件大小: 7.45MB | 文件类型: -
经典著作,不用多介绍了。 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.

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评论信息

  • ht11221006 :
    非常好!不是扫描成图片的,非常感谢!找了好久才找到~推荐!
    2015-03-09
  • u013684437 :
    我表示非常感谢!!对我很有用处,推荐!
    2014-12-01
  • jy03365266 :
    graphical model的经典教材,而且不是扫描版,非常不错,多谢了。
    2014-08-13
  • happog :
    真心不错,文字版本的。最理想的版本。
    2014-07-08
  • sunny13love :
    谢谢分享,很不错。
    2014-06-17

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