- There is a fragile construction in cpt2. 

- demo() ends in a strange way

- Look into discretization of variables; calculate mean/var when a variable is ordinal.

- Use gRbase naming convention: varNames etc.

- Create cpt from a dataframe/table...

- Postpone triangulation until evidence E=e* and a set U of nodes of interest are specified.
  Form ancestral graph G' for U \cup E
  Enter evidence
  Remove all edges from evidence nodes
  Update potentials (potentials with evidence have their dimension reduced)
  Triangulate etc.
  
- Optimal triangulation: Lauritzen Sheehan, Statistical science, graphical models in genetics

- Simulate in cliques; gives MC propagation as an alternative
  propagation scheme



