On separation criterion and recovery algorithm for chain graphs.
In Uncertainty in Artificial Intelligence.
Proceedings of the 12th Conference
(E. Horvitz, F. Jensen eds.), Morgan Kaufmann, San Francisco 1996, pp. 509-516.
The paper describes in a simple way some results of two (later published) papers:
- M. Studeny:
A recovery algorithm for chain graphs.
International Journal of Approximate Reasoning
17 (1997), n. 2-3, pp. 265-293.
- M. Studeny and R. R. Bouckaert:
On chain graph models for description of conditional
The Annals of Statistics
26 (1998), n. 4, pp. 1434-1495.
- and illustates them by examples.
- A direct graphical
separation criterion for chain graphs which generalizes the Pearl's
d-separation criterion for Bayesian networks is introduced (recalled).
It is equivalent to the classic moralization criterion for chain graphs
and complete in the sense that for every chain graph there exists
a strictly positive probability distribution satisfying exactly the
independency statements derivable from the chain graph according to
the separation criterion. Every class of Markov equivalent chain graphs
can be uniquely described by a natural representative, called the
largest chain graph. A recovery algorithm, which on basis of the
(conditional) dependency model induced by a chain graph finds the
corresponding largest chain graph, is presented.
- AMS classification 68T30, 62H05
- chain graph
- Markov equivalence
- largest chain graph
- recovery algorithm
pdf copy (converted postscript version) (1167kB) is available.