In the previous chapters, we focused on Bayesian models with Bernoulli likelihoods and beta priors. Most books on Bayesian statistics begin with Bernoulli-Beta models because they provide an intuitive and accessible set of examples to learn how the Bayes rule works and understand the fundamentals of Bayesian computing. However, as we work toward other types of Bayesian models and situations with multiple parameters, it will be helpful to learn a few tricks for dealing with additional distributions.