DTs: Dynamic Trees

A dynamic tree model specifies a prior over a large number of trees, each one of which is a tree-structured belief net (TSBN). Our aim is to retain the advantages of tree-structured belief networks, namely the hierarchical structure of the model and (in part) the efficient inference algorithms, while avoiding the "blocky" artifacts that derive from a single, fixed TSBN structure. One use for DTs is as prior models over labellings for image segmentation problems. Section 2 of the paper gives the theory of DTs, and experiments are described in section 3. 2 Theory There are two essential components that make up a dynamic tree network (i) the tree architecture and (ii) the nodes and conditional probability tables (CPTs) in the given tree. We consider the architecture question first.