Probabilistic Graphical Models pdf epub mobi txt 電子書 下載2025
- 機器學習
- 概率圖模型
- Graph-Model
- 數學
- MachineLearning
- 計算機
- 數據挖掘
- 算法
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.
具體描述
讀後感
第一次接触到概率图是在PRML第八章，讲的不是很详细，可以说不详细，就是说了说啥是概率图而已。然后再cousra上看到这门课没有坚持下去。幸好，我T大有一门课就是用这书作为教材，我就选修了这门课。不上则已，一上而一发不可收。 清晰的框架无人企及。 把概率图分为表示推断与...
評分8.9mb完整电子版 万众期待 国内首发 http://ishare.iask.sina.com.cn/f/37600277.html 抱歉，你的评论太短了 抱歉，你的评论太短了 抱歉，你的评论太短了 够了吧
評分http://pan.baidu.com/s/1gd98yx9 其他的就不说了, 结合视频学习吧 感觉还是挺难的, 但是不学习的话, 好多地方都会遇到瓶颈. 抱歉，你的评论太短了 抱歉，你的评论太短了 抱歉，你的评论太短了 抱歉，你的评论太短了 抱歉，你的评论太短了 抱歉，你的评论太短了 抱歉，你的...
評分有保留的推荐。 书的优点：很全，较新，成体系，连贯性很好。 书的缺点：错误挺多，抽象晦涩，理论性很强。 我个人是做视频的高层信息理解分析的，偶然之间接触到概率图模型的几个算法，后来跟着实验室的其他老师和组里的同学一起学了这本书。听了大家的讲解，让我收获很多，...
評分第一次接触到概率图是在PRML第八章，讲的不是很详细，可以说不详细，就是说了说啥是概率图而已。然后再cousra上看到这门课没有坚持下去。幸好，我T大有一门课就是用这书作为教材，我就选修了这门课。不上则已，一上而一发不可收。 清晰的框架无人企及。 把概率图分为表示推断与...
用戶評價
非常全麵，可以配閤coursera上daphne koller的課一起學
评分為什麼到我們這屆就不是Daphne教瞭……
评分巨細無遺。
评分垃圾！囉嗦，定義不清晰，redundant。
评分全麵，體係化，適閤做深入研究。
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