Hierarchical Bayesian models leverage hierarchical structures to refine prior distributions and improve Bayesian inference. By nesting parameters within a hierarchy, these models capture dependencies and correlations within data. They provide a flexible framework to model complex systems, quantify uncertainty, and make better predictions by incorporating prior knowledge and data into the modeling process.
