Probabilistic reasoning in intelligent systems: networks of plausible inference
Probabilistic reasoning in intelligent systems: networks of plausible inference
Chain graphs: semantics and expressiveness
ECSQARU '95 Proceedings of the European Conference on Symbolic and Quantitative Approaches to Reasoning and Uncertainty
Equivalence and synthesis of causal models
UAI '90 Proceedings of the Sixth Annual Conference on Uncertainty in Artificial Intelligence
On separation criterion and recovery algorithm for chain graphs
UAI'96 Proceedings of the Twelfth international conference on Uncertainty in artificial intelligence
Parallell interacting MCMC for learning of topologies of graphical models
Data Mining and Knowledge Discovery
Faithfulness in chain graphs: The discrete case
International Journal of Approximate Reasoning
Finding consensus Bayesian network structures
Journal of Artificial Intelligence Research
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The paper gives a few arguments in favour of use of chain graphs for description of probabilistic conditional independence structures. Every Bayesian network model can be equivalently introduced by means of a factorization formula with respect to chain graph which is Markov equivalent to the Bayesian network. A graphical characterization of such graphs is given. The class of equivalent graphs can be represented by a distinguished graph which is called the largest chain graph. The factorization formula with respect to the largest chain graph is a basis of a proposal how to represent the corresponding (discrete) probability distribution in a computer (i.e. 'parametrize' it). This way does not depend on the choice of a particular Bayesian network from the class of equivalent networks and seems to be the most efficient way from the point of view of memory demands. A separation criterion for reading independences from a chain graph is formulated in a simpler way. It resembles the well-known d-separation criterion for Bayesian networks and can be implemented 'locally'.