Hierarchical Bayesian Models are a class of statistical models that allow for the incorporation of multiple levels of uncertainty and variability, enabling more nuanced inferences by modeling data at different levels of hierarchy. These models are particularly useful for analyzing data with nested structures, such as repeated measurements or grouped data, by allowing parameters to be shared across different levels of the hierarchy, improving estimates and predictions.