On Separation Criterion and Recovery Algorithm for Chain Graphs
نویسنده
چکیده
Chain graphs {CGs) give a natural unifying point of view on Markov and Bayesian net works and enlarge the potential of graphi cal models for description of conditional in dependence structures. In the paper a di rect graphical separation criterion for CGs which generalizes the d-separation criteri on for Bayesian networks is introduced (re called). It is equivalent to the classic mo _ r alization criterion for CGs and complete m the sense that for every CG there exists a probability distribution satisfying exactly in dependencies derivable from the CG by the separation criterion. Every class of Markov e quivalent CGs can be uniquely described by a natural representative, called the largest CG. A recovery algorithm, which on basis of the (conditional) dependency model given by a CG finds the corresponding largest CG, is p
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تاریخ انتشار 1996