Hierarchical products process parameters

The hierarchical Bayesian modeling methodology begins with separating the unknowns into two groups (1) process variables (actual physical quantities of interest) and (2) model parameters (quantities introduced in model development). Three distributions are specified (1) [data process, parameters], (2) [process parameters], and (3) [parameters] to give us the posterior [process, parameters data] which is proportional to the product of these three distributions. One simple example is given as follows ... 

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