Hierarchical Bayesian models are statistical models that allow for the modeling of data with multiple levels of variability by introducing parameters at different levels. These models enable the integration of information across different groups or populations, making them particularly useful for data with complex structures. By using prior distributions at each level, they allow for sharing information and improving estimates, especially when data is sparse.
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