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I am a self-learner and have done several machine learning courses but diving into Bayesian or Probabilistic Graphical Models I feel like my prior knowledge is inadequate. I have done some Probability and Statistics in high-school and engineering introductory course but I still feel a gap of knowledge, maybe because I have forgotten earlier concepts. Although I can work with the following equation, I still don't intuitively understand it.

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What are some subjects and their resources that I should study in order to understand them?

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  • $\begingroup$ This is Bayes' rule, the cornerstone of Bayesian statistics. $\endgroup$ – Knarpie Jan 10 at 9:28
  • $\begingroup$ Yes, I understand that. The lower term equals to P(X) after the integral is resolved. But it is not intuitive to me. I need some resources to understand this and other topics which will help me understand Bayesian Models better. $\endgroup$ – BitsAndPieces Jan 10 at 13:24

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