  This paper describes a generalization of previous methods for constructing
tree-structured belief network with hidden variables. The major new feature of
the described method is the ability to produce a tree decomposition even when
there are errors in the correlation data among the input variables. This is an
important extension of existing methods since the correlational coefficients
usually cannot be measured with precision. The technique involves using a
greedy search algorithm that locally minimizes an error function.
