贝叶斯网络教程
对于初学者来说，学习贝叶斯网络最好从哪本书开始？
For a beginner, which is the best book to start with for studying Bayesian Networks?
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对于初学者来说，学习贝叶斯网络最好从哪本书开始？
For a beginner, which is the best book to start with for studying Bayesian Networks?
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我推荐 Daphne Koller 和 Nir Friedman 的“概率图形模型”。 这是一本关于有向（贝叶斯网络）和无向（马尔可夫网络）图形模型的优秀入门到中级手册。 给出的例子详尽且易于理解。
I would recommend "Probabilistic Graphical Models" by Daphne Koller and Nir Friedman. Its an excellent starter-to-intermediate handbook on both directed (Bayesian Networks) and undirected (Markov Networks) graphical models. The examples given are elaborate and easy to understand.
关于通用机器学习的一本好书是 1。 但对BN来说却很轻。 我还没有读过 [2]，但我读过他写的 [3]，这很好（所以，[2] 可能是 dwf 推荐的好书）。 除非你正在攻读博士学位，否则我根本不会推荐珀尔的书！
不过，我实际上会推荐 Kevin Murphy 的在线教程“图形模型和贝叶斯网络简介”[4]。 学习 BN 的最好方法是阅读这篇文章，下载他的 Matlab 工具箱 [5]，并在十分钟内构建你自己的 BN。
A good book on general machine learning is 1. But it is quite light on BN. I haven't read [2] but I have read [3] by him which is good (so, [2] is likely to be good as recommended by dwf). I would not recommend Pearl's book at all unless you are doing your Ph.D.!
However, I actually would recommend the online tutorial "A Brief Introduction to Graphical Models and Bayesian Networks" by Kevin Murphy [4]. The best way to learn BN is to read this, download his Matlab toolbox [5] and build your own BN in ten minutes.
您应该查找 AI（人工智能）书籍。 我在人工智能“现代方法”中了解了贝叶斯。
You should check for AI (Artificial Intelligence) books. I've learn about Bayesian in Artificial Intelligence "A modern approach".
这本在线书籍在机器学习的各个方面对我都非常有帮助，包括贝叶斯推理：
http://www.inference.phy.cam.ac.uk/mackay/itila/book.html
假设您熟悉基本概率论，那么它是一个很棒的资源。
This online book has been extremely helpful for me in all aspects of machine learning, including Bayesian inference:
http://www.inference.phy.cam.ac.uk/mackay/itila/book.html
Granted you are familiar with basic probability theory, its a great resource.
到目前为止提到的所有书籍都非常好。 珍珠的一般被认为有点难以遵循，它也相当昂贵，但如果你能驾驭它，所有的力量都给你。
我真的真的建议您查看 Chris Bishop 的书，模式识别和机器学习。 我认为这无疑是教科书中图形模型的最佳处理方式，至少在 迈克尔·乔丹完成并出版了有关该主题的书。
All the books mentioned so far are pretty good ones. Pearl's is generally regarded as being a bit hard to follow, it's also quite expensive, but if you can manage it, all the power to you.
I'd really really recommend you check out Chris Bishop's book, Pattern Recognition and Machine Learning. I think it's far and away the best treatment you're going to get of graphical models in a textbook, at least until Michael Jordan finishes and publishes his book on the subject.
在我看来，这个领域最好的教授是这两个人：链接文本 不好。 Andrew 和 链接文本 Pallab Dasgupta 教授。
我一直在观看他们在 BBN 上的所有教程，它们非常有用。只要点击链接，你就会发现更多关于这两个有趣的人的人工智能讲座。
和他们一起快乐学习，
麦克风
The best professors in this fields are by my point of view these 2 guys:link text Ng. Andrew and link text Prof. Pallab Dasgupta.
I have been watching all their tutorials on BBN and they were very usefull.Just follow the links and you will find more AI lectures with this 2 interesting guys.
Have fun learning with them,
Mike
Pearl 于 1988 年出版的智能系统中的概率推理是贝叶斯网络领域被引用最多的著作之一。 我发现它很清楚。 也就是说，自 1988 年以来，该领域已经做了很多工作。用最近的作品来补充本书是明智的。
Pearl's 1988 Probabilistic Reasoning in Intelligent Systems is the one of the most cited works on Bayesian Networks. I found it quite clear. That said, a lot has been done in the field since 1988. It would be wise to supplement this book with more recent works.
米切尔的机器学习是人工智能领域极其重要的入门读物。 我记得它涵盖了贝叶斯网络，用了一整章来介绍它。
我还会查看 Weka 的贝叶斯网络类来了解实际的实现。 如果您不了解 Weka，请查看此处：http://www .cs.waikato.ac.nz/ml/weka/
Mitchell's Machine Learning is an extremely important primer in the area of AI. It covers Bayesian Networks, devoting, as I recall, an entire chapter to it.
I'd also check out Weka's Bayesian Network class to understand a practical implementation. If you don't know about Weka, check it out here: http://www.cs.waikato.ac.nz/ml/weka/