Probabilistic Graphical Models: Principles and Techniques (Hardcover)

Daphne Koller, Nir Friedman

  • 出版商: MIT
  • 出版日期: 2009-08-01
  • 售價: $5,360
  • 貴賓價: 9.5$5,092
  • 語言: 英文
  • 頁數: 1270
  • 裝訂: Hardcover
  • ISBN: 0262013193
  • ISBN-13: 9780262013192
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商品描述

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.

Adaptive Computation and Machine Learning series

商品描述(中文翻譯)

大多數任務需要一個人或自動系統進行推理,根據可用信息得出結論。本書介紹的概率圖模型框架提供了一種通用方法來完成這個任務。這種方法是基於模型的,允許構建可解釋的模型,然後通過推理算法對其進行操作。這些模型還可以從數據中自動學習,使得在手動構建模型困難甚至不可能的情況下也能使用這種方法。由於不確定性是大多數現實應用中不可避免的一個方面,本書專注於概率模型,這些模型將不確定性明確化,並提供更貼近現實的模型。

《概率圖模型》討論了各種模型,包括貝葉斯網絡、無向馬爾可夫網絡、離散和連續模型,以及處理動態系統和關聯數據的擴展。對於每個模型類別,本書描述了三個基本基石:表示、推理和學習,介紹了基本概念和高級技術。最後,本書考慮了在不確定性下進行因果推理和決策的提出框架的應用。

每章的主要內容提供了關鍵思想的詳細技術發展。大多數章節還包括附加材料的方框:技巧方框,描述技術;案例研究方框,討論與文本中描述的方法相關的實證案例,包括在計算機視覺、機器人技術、自然語言理解和計算生物學等領域的應用;以及概念方框,介紹從該章節材料中提取的重要概念。教師(和讀者)可以根據自己的需求將章節分組成各種組合,從核心主題到更技術先進的材料。

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