next up previous contents
Next: Conjugate priors Up: Bayesian statistics Previous: Constructing probabilistic models   Contents

Hierarchical models

Many models include groups of parameters that are somehow related or connected. This connection should be reflected in the prior chosen for them. Hierarchical models provide a useful tool for building priors for such groups. This is done by giving the parameters a common prior distribution which is parameterised with new higher level hyperparameters [16].

Such a group would typically include parameters that have a somehow similar status in the model. Hierarchical models are well suited for neural network related problems because such connected groups emerge naturally, like for example the different elements of a weight matrix.

The definitions of the components of the Bayesian nonlinear switching state-space model in Chapter 5 contain several examples of hierarchical priors.

Antti Honkela 2001-05-30