A recovery algorithm for chain graphs.
International Journal of Approximate Reasoning
17 (1997), n. 2-3, pp. 265-293.
- The class of chain graphs involving both undirected
graphs and directed acyclic graphs
was introduced in middle eighties for description of probabilistic
conditional independence structures. Every class of Markov equivalent
chain graphs (that is, the graphs describing the same conditional
independence structure) has a natural representative, which is called the
largest chain graph. The paper presents a recovery algorithm, which
on the basis of the conditional independence structure induced by a chain
graph finds the largest chain graph, representing the corresponding class
of Markov equivalent chain graphs. As a byproduct an undirect (implicit)
graphical characterization of the graphs, which are the largest chain
graphs (for a class of Markov equivalent chain graphs) is obtained, and
an algorithm changing every chain graph into the largest chain graph of
the corresponding equivalence class is described.
- AMS classification 68T30, 62H05
- chain graph
- Markov equivalence
- largest chain graph
- pool-component principle
scanned pdf copy (1482kB) is available.
The paper builds on the following works:
- M. Frydenberg: The chain graph Markov property.
Scandinavian Journal of Statistics
17 (1990), n. 3, pp. 333-353.
- Ch. Meek: Causal inference and causal explanation
with background knowledge. In Uncertainty in Artificial
Intelligence 11 (B. Besnard, S. Hanks eds.) Morgan Kaufmann 1995,