Description: A hierarchical model is a statistical model that organizes data into multiple levels of structure, allowing for a richer and more complex representation of variability in the data. This approach is particularly useful in situations where data is grouped or nested, such as in studies involving multiple measurements of individuals within groups. Hierarchical models capture both variability between groups and variability within each group, providing a deeper understanding of underlying relationships. In the context of machine learning, hierarchical models can be used to process structured data, where the features of the data can be organized at different levels of abstraction. This facilitates learning complex patterns and inference in contexts where data has a natural hierarchical structure. Additionally, in supervised learning, these models can improve prediction accuracy by considering the structure of the data. In the realm of edge inference (Edge AI), hierarchical models can optimize data processing on resource-limited devices, enabling quick and efficient decisions based on the hierarchy of available information.
