Chain Graphs for Learning
  Chain graphs combine directed and undirected graphs and their underlying
mathematics combines properties of the two. This paper gives a simplified
definition of chain graphs based on a hierarchical combination of Bayesian
(directed) and Markov (undirected) networks. Examples of a chain graph are
multivariate feed-forward networks, clustering with conditional interaction
between variables, and forms of Bayes classifiers. Chain graphs are then
extended using the notation of plates so that samples and data analysis
problems can be represented in a graphical model as well. Implications for
learning are discussed in the conclusion.
