Hierarchical Bayesian models are statistical models that use Bayesian inference to incorporate multiple levels of variability in data, allowing for the estimation of parameters at different levels of a hierarchy. These models are particularly useful when dealing with grouped or clustered data, as they can borrow strength from related groups to improve estimates and account for uncertainty in a structured way.
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