Hierarchical models are statistical models that are structured in layers, allowing for the incorporation of multiple levels of variability and dependencies. They enable the analysis of data that is organized at different levels, such as individuals nested within groups, making them particularly useful in capturing relationships and variability across those levels. This structure allows for more complex modeling of real-world situations, connecting to various aspects like probability distributions, model comparison, and sampling techniques.
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