Bayesian Hierarichal Model
Introduction
A Bayesian hierarchical model, is a statistical model that incorporates multiple levels of variability or hierarchy in its structure. It is particularly useful when dealing with complex data that exhibits nested structures or when there is a need to model variability at different levels of aggregation. For instance, in forecasting demand for products in a retail chain, a hierarchical Bayesian model may include hierarchical components at multiple levels, such as product-level trends, store-level seasonality, and regional-level effects. By accounting for the hierarchical nature of the data, the model can improve the accuracy of demand forecasts and capture variability across different levels of aggregation.