ABSTRACT
Unit 2 serves as a critical bridge to applying the fundamental concepts from Unit 1 in the more sophisticated model settings of Unit 3 and beyond. In Unit 1, we learned to think like Bayesians and to build some fundamental Bayesian models in this spirit. Further, by cranking these models through Bayes' Rule, we were able to mathematically specify the corresponding posteriors. Those days are over. Though merely hypothetical for now, some day (starting in Chapter 9) the models we'll be interested in analyzing will get too complicated to mathematically specify. Never fear – data analysts are not known to throw up their hands in the face of the unknown. When we can't know or specify something, we approximate it. In Unit 2 we'll explore Markov chain Monte Carlo simulation techniques for approximating otherwise out-of-reach posterior models.
