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What is Hierarchical Models in Bayesian Analysis about?

The document discusses hierarchical models in Bayesian statistics, focusing on the specification of joint priors and the derivation of marginal posteriors for inference. It presents a hierarchical prior for normal data, detailing the likelihood and the process of approximating the posterior using sampling-based algorithms. Additionally, it explores applications of hierarchical modeling in various fields, emphasizing the benefits of borrowing strength across subgroups for efficient estimation.

Author

jackyko0319

Language

EN