The hierarchical model jointly optimizes global and domain-specific parameters, using parameters built from pooled data as priors for domain-specific parameters.
instead of using smoothing to encourage parameters to be closer to zero, it encourages domainspecific model parameters to be closer to the corresponding global parameters, while a zero mean Gaussian prior is still applied for global parameters.