Heterogeneity and Hierarchical Models: Understanding Pooled, Unpooled, and Hierarchical Approaches
Published:
Hierarchical models are powerful tools in statistical modeling and machine learning, enabling us to represent data with complex dependency structures. These models are particularly useful in contexts where data is naturally grouped or exhibits multi-level variability. A critical aspect of hierarchical models lies in their hyperparameters, which control the relationships between different levels of the model.