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I looked at Koller & Friedman's Probabilistic Graphical Models, but their use of non-standard notation is prompting me to see if there is anything else out there.

I'd like to find an introductory book that is comprehensive and accessible. I'm not looking for something that surveys the state of the art, just something that focuses on motivation and development.

In particular, I'm looking for something that would cover (Dynamic) Bayesian Networks, Kalman Filters, Hidden Markov Models (or Markov Models in general) and Markov Random Fields.

Anyone have any recommendations?

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up vote 3 down vote accepted

I would highly recommend Christopher Bishop's Pattern Recognition and Machine Learning. It's an excellent introductory book and used to teach several graduate classes on the topics you have listed.

And as luck would have it, the book's sample contains the graphical models chapter!

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