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.
Prof. Daphne Koller from Stanford has a live online course. One just started a few days ago: https://www.coursera.org/course/pgm .
Her book with Nir Friedman is an excellent comprehensive text book for this subject and an Amazon bestseller (don't worry I don't work for them): http://www.amazon.com/Probabilistic-Graphical-Models-Principles-Computation/dp/0262013193/
I would like to suggest the tutorial from Christopher Bishop. Just search for the name and probabilistic graphical model. You will find it. Christopher Bishop is the author of Pattern Recognition and Machine Learning. He has provided a free downloadable pdf of the book chapter on probabilistic graphical model from his website and lectures on this topic including at the Machine Learning Summer School in 2013, held at the Max Planck Institute for Intelligent Systems.
Also, I used the presentations from some other universities on this topic, which may have a number of various course names such as the introduction to graphical models. You may find one that suits your needs.