Monthly Archives: February 2008

Hierarchical Bayes Models – Key Steps for Estimation

A model is hierarchical (random coefficient) if the paramters in a GLM (General Linear Model) are themselves structured to be having a prior distribution involving parameters of its own (hyper parameters).

In a practical situation, this means that the posterior probability (or posterior odds) are a function of (prior odds) x (marginal distribution of hyper parameter1 – parameter describing centrality of the distribtion ) x (marginal distribution of hyper parameter2 – parameter describing the variability of the distribution).

There are no closed form solution for the calculation of posterior odds.

So the estimation procedures involve simulation based calculations:

– in the first step, we use estimation of a the basic GLM model parameters using existing data in the usual sense of the estimation

– in the second step, we use estimation of the hyper parameters based on outputs of first step which provides

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