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I recently started taking Probabilistic Graphical Models on coursera, and 2 weeks after starting I am starting to believe I am not that great in Probability and as a result of that I am not even able to follow the first topic (Bayesian Network). That being said I want to make an effort to learn this course, so can you suggest me some other resources for PGM or for Probability which can be helpful in understanding this course.

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You could try reading Pearl's 1988 book Probabilistic Reasoning in Intelligent Systems, which gives much background and insights into the bayesian way of seeing things. Concerning probability theory, you don't really need that much theory beside the three basic laws of probability and the definition of conditional probabilities, which are both simple and usually taught in school.

This book is very influential to the way AI has developed over the last 20 years. The author was awarded the Turing Award this year.

Also there's a rather new book by Koller and Friedman: Probabilistic Graphical Models (2009). You should already know about this one, since the course is probably held by Daphne Koller again. This book includes many more recent results and covers more ground, in more detail. It can be very demanding in parts. It probably also shares examples with the course.

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I second the Koller/Friedman book. Currently it is THE textbook about graphical models. –  Bernhard Kausler Jan 25 '13 at 12:52

PGMs are a bit advanced if you don't have a good grasp of probability theory. A more introductory class is Statistics 1, might be better to start there.

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How will statistics course will me with Probability theory? –  Dude Sep 26 '12 at 16:49
Because statistics and probability are very intertwined. An intro statistics class normally has a section on basic probability theory. –  robbrit Sep 26 '12 at 18:34

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