Abstract:   Modern astronomical data sets provide a wealth of data and well-grounded theoretical models describing those data. Our models provide a deeper (multi-level) statistical hierarchy which is allowing researchers to leverage information about the hyper-parameters and hyper-priors in astronomical Bayesian analyses. I will discuss two recent applications of Bayesian Hierarchical Modeling to address how galaxies evolve in clustered environments. In the first example, I will show how Bayesian Hierarchies are enabling us to place observational constraints on the growth of dim red galaxies in clusters over the last few billion years. In the second case, I will show how we used Bayesian Hierarchies to discover a physically meaningful latent variable in the relationship between the underlying halo mass of a galaxy cluster and the stellar mass of the brightest central galaxy. I will also touch on other areas of astronomical research where Bayesian Hierarchies are making an impact. I will conclude with a discussion of current and future challenges, including the next generation of high dimensional models as well as improvements in our statistical modeling techniques.