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Is Koller's "Probabilistic Graphical Models" suitable as a textbook? Or is there another book which is more recommendable as textbook for a master-course?

Disclaimer: cross-posted from quora.com, where I got no answer.

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    $\begingroup$ I can't tell you anything about the book. But I noticed that Daphne Koller / Stanford is offering a free online course on probablistic graphical models. You should sign up! $\endgroup$ Commented Jan 11, 2012 at 14:36

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Yes, it's written as such and contains sample questions, for which you can request the answers here

You might also want to have a look at Pattern Recognition and Machine Learning by Chris Bishop and Information Theory, Inference and Learning Algorithms by David MacKay, which can also be downloaded for free. Both of these cover some aspects of graphical models as well as giving a general insight into probabilistic methods.

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    $\begingroup$ how do you request for the solutions? $\endgroup$ Commented Jul 27, 2014 at 23:57
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    $\begingroup$ Try here: mitpress.mit.edu/txbkreq/195422 $\endgroup$
    – tdc
    Commented Jul 28, 2014 at 12:29
  • $\begingroup$ @tdc do you have a PDF of the solution manual? It seems like I need to have an "instructor's account" to request solutions $\endgroup$ Commented Mar 27, 2021 at 22:07
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I spent a little while reading the first couple of chapters of Koller & Friedman, and I wasn't happy with it as an introductory text. On several occasions, the book gives a motivating example, but the example cannot be understood without background material later in the chapter. This kind of exposition works for me only if the example explicitly says what upcoming material will be relevant; otherwise, the examples are just incomprehensible magic.

That said, it's a hefty tome, and probably an excellent reference for practitioners.

A student might have better luck with Neapolitan, "Learning Bayesian Networks".

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I would prefer the book Graphical Models by Steffen L. Lauritzen, and his lecture at Oxford.

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