Bayesian Hierarchical Models are statistical models that allow for the analysis of data with multiple levels of variability. They incorporate prior beliefs and information at different levels, enabling researchers to make inferences about complex data structures. This approach is particularly useful when dealing with grouped or nested data, where observations are not independent. In these models, parameters are organized hierarchically, meaning that parameters at one level can influence those at another. This structure helps to borrow strength from related groups, improving estimates and predictions. Applications of Bayesian Hierarchical Models can be found in fields like psychology, ecology, and economics.