Hierarchical Bayesian models are a class of statistical models that use Bayesian inference to analyze data with multiple levels of variability. These models are particularly useful when dealing with grouped or nested data structures, as they allow for the incorporation of prior distributions at different levels, capturing the relationships between parameters across different groups. This multi-level approach enhances the ability to make inferences about the overall population while accounting for individual group differences.
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