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2025-01-31
Probabilistic Graphical Models 2025 pdf epub mobi 電子書 下載
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
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
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...
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
寫在這裏以激勵我還有此四本書沒有讀。。。
評分很厚很全麵的書,不過就是太多內容瞭。分配到每個話題的卻有不是特彆多,適閤參考看。
評分難難難 看不懂 實際沒看完
評分巨細無遺。
評分渣就一個字。廢話太多,又不cover領域前沿。講的都是沒用的,好東西沒講到。不如直接看Martin Wainwright, Michael Joradn的review論文
Probabilistic Graphical Models 2025 pdf epub mobi 電子書 下載