Learning Link-Probabilities in Causal Trees
  A learning algorithm is presented which given the structure of a causal tree,
will estimate its link probabilities by sequential measurements on the leaves
only. Internal nodes of the tree represent conceptual (hidden) variables
inaccessible to observation. The method described is incremental, local,
efficient, and remains robust to measurement imprecisions.
