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.
發表於2024-11-22
Probabilistic Graphical Models 2024 pdf epub mobi 電子書 下載
有保留的推薦。 書的優點:很全,較新,成體係,連貫性很好。 書的缺點:錯誤挺多,抽象晦澀,理論性很強。 我個人是做視頻的高層信息理解分析的,偶然之間接觸到概率圖模型的幾個算法,後來跟著實驗室的其他老師和組裏的同學一起學瞭這本書。聽瞭大傢的講解,讓我收獲很多,...
評分第一次接觸到概率圖是在PRML第八章,講的不是很詳細,可以說不詳細,就是說瞭說啥是概率圖而已。然後再cousra上看到這門課沒有堅持下去。幸好,我T大有一門課就是用這書作為教材,我就選修瞭這門課。不上則已,一上而一發不可收。 清晰的框架無人企及。 把概率圖分為錶示推斷與...
評分http://pan.baidu.com/s/1gd98yx9 其他的就不說瞭, 結閤視頻學習吧 感覺還是挺難的, 但是不學習的話, 好多地方都會遇到瓶頸. 抱歉,你的評論太短瞭 抱歉,你的評論太短瞭 抱歉,你的評論太短瞭 抱歉,你的評論太短瞭 抱歉,你的評論太短瞭 抱歉,你的評論太短瞭 抱歉,你的...
評分http://pan.baidu.com/s/1gd98yx9 其他的就不說瞭, 結閤視頻學習吧 感覺還是挺難的, 但是不學習的話, 好多地方都會遇到瓶頸. 抱歉,你的評論太短瞭 抱歉,你的評論太短瞭 抱歉,你的評論太短瞭 抱歉,你的評論太短瞭 抱歉,你的評論太短瞭 抱歉,你的評論太短瞭 抱歉,你的...
評分8.9mb完整電子版 萬眾期待 國內首發 http://ishare.iask.sina.com.cn/f/37600277.html 抱歉,你的評論太短瞭 抱歉,你的評論太短瞭 抱歉,你的評論太短瞭 夠瞭吧
圖書標籤: 機器學習 概率圖模型 Graph-Model 數學 MachineLearning 計算機 數據挖掘 算法
救急囫圇吞棗一下,後來已經改變思路瞭XD很全麵的內容,有機會再二周目
評分看看還是覺得挺喜歡的,很成體係。唯一的缺點是太抽象瞭。再者,裏麵的錯誤也是不少的,包括一些排版錯誤和一些公式推導的錯誤。但是,書內容很全,也算是很新吧。
評分the book is glowing with intelligence, and still after two years
評分非常全麵,可以配閤coursera上daphne koller的課一起學
評分救急囫圇吞棗一下,後來已經改變思路瞭XD很全麵的內容,有機會再二周目
Probabilistic Graphical Models 2024 pdf epub mobi 電子書 下載