I have basic understanding of Stats and till now have worked with Linear & Logistic Regression, Random Forests etc. Introduction to Statistical Learning was my go to book.I never worked with or studied Bayesian Statistics and was blissfully unaware of the whole Frequentist vs Bayesian thinking/debate.
Now I am working on a new project using Bayesian networks and I am really struggling to grasp the concepts. My main issue is that when looking through the different modelling techniques I found that all things like Regression, Decision trees etc are also done through Bayesian Statistics. This made me realize that Bayesian statistics is not just about new techniques but a way of rethinking everything I studied before.
Can someone provide a simple road map of the different techniques under both kinds of stats and when to use which.
I have not studied Statistics formally but now working in machine learning I am learning as I go. Bayesian Statistics looks daunting.