In contrast to frequentist approaches, Bayesian methods provide a principled framework for combining data with prior information when making inferences. Bayesian methods allow for more precision in small samples. In large samples, Bayesian nonparametric/machine learning methods can capture complex, nonlinear relationships in the data to produce accurate predictions and uncertainty quantification. Bayesian methods are widely used to solve complex inference problems in microsimulations, genomics, causal inference, missing data problems, and more.