Hierarchical Bayesian models are statistical models that allow for multiple levels of variability and uncertainty in the data by incorporating different layers of parameters that can vary across groups or levels. This structure helps to borrow strength from related data, making it particularly useful for complex datasets where observations are grouped, such as in clinical trials or educational assessments. The hierarchical aspect enables the modeling of both individual-level and group-level variations, leading to more robust inferences.
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