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- W174811566 abstract "Empirical studies clearly demonstrate the effectiveness of the nested jointree (NJT) representation in probabilistic inference. A NJT is a traditional Markov network (MN) together with a possible local MN nested in each clique. These nested MNs can themselves contain other nested MNs in a recursive manner. However, the NJT representation is not necessarily a faithful representation of a given Bayesian network (BN). This means that the effectiveness of a NJT has been demonstrated while only exploiting some of the independency information available in the given BN. In this paper, we introduce a new kind of probabilistic network, called a hierarchical Markov network (HMN). A HMN is a hierarchy of MNs. We give an algorithm to transform a BN into a canonical HMN. The main result of this paper is that the constructed HMN is unique and equivalent to the input BN. Since HMNs are a faithful representation of BNs, a query may be optimized using independencies in a HMN that otherwise would have gone unrepresented in a NJT approach." @default.
- W174811566 created "2016-06-24" @default.
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- W174811566 date "2001-01-01" @default.
- W174811566 modified "2023-09-27" @default.
- W174811566 title "On Undirected Representations of Bayesian Networks" @default.
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