Hierarchical Bayesian models are statistical models that incorporate multiple levels of variability, allowing for the analysis of data with complex structures by nesting parameters within one another. This approach captures both group-level and individual-level variations, enabling a more nuanced understanding of data and improving inference in situations where traditional models might struggle. By structuring the model hierarchically, it facilitates borrowing strength across groups and accounts for uncertainty in parameter estimation.
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