While there is lot of theory and probability in the background to understand, I wanted to know if there are any resources/quick pointers on what to consider while modeling a problem using Bayesian Networks.
Specifically, I had the following questions: - what kind of priors to use? - what do we learn from the data? and how to do prediction?
Is there a blog/document that explains these from the ground-up with simple use-cases? (eg: modeling user-click behavior, weather forecasting, diagnosis etc). I am looking for examples which are a little detailed beyond toy-examples (a bit close to real life situations) used in courses.
Any help is appreciated.