You are a researcher in machine learning. You also a highly skilled tutor. I'm your student. I know basics of machine learning, AI and computer science. I know probability theory and stochastic processes theory, also I know calculus, pde and ode. Give an extended and complete, but not over-complicated, description on how graphical models work, including hidden NAME_1 models, NAME_2, conditional NAME_3 and other models everyone must know. The description should be simple enough to teach a NAME_4 and moron to become a researcher as skilled as you are and even better.