First Stats Post

This is a paper I had to read for a class I am taking. It outlines hierarchical models, focusing on Bayesian hierarchical models. It is long and dense but the main points are in the first couple sections. It is a pretty sweet way to analyze data and makes a lot of sense for complicated problems. The main point is that breaking models down into separate components of uncertainty allows for a very adaptable framework. Then the MCMC methods can allow for fitting these super complex models.