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ON FINDING MINIMUMDIAMETER CLIQUE TREES
 NORDIC JOURNAL OF COMPUTING 1(1994), 173–201.
, 1994
"... A cliquetree representation of a chordal graph often reduces the size of the data structure needed to store the graph, permitting the use of extremely efficient algorithms that take advantage of the compactness of the representation. Since some chordal graphs have many distinct cliquetree represen ..."
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A cliquetree representation of a chordal graph often reduces the size of the data structure needed to store the graph, permitting the use of extremely efficient algorithms that take advantage of the compactness of the representation. Since some chordal graphs have many distinct cliquetree
Macroscopic models of clique tree growth for Bayesian networks
 In Proceedings of the TwentySecond National Conference on Artificial Intelligence (AAAI07
, 2007
"... In clique tree clustering, inference consists of propagation in a clique tree compiled from a Bayesian network. In this paper, we develop an analytical approach to characterizing clique tree growth as a function of increasing Bayesian network connectedness, speci cally: (i) the expected number of mo ..."
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Cited by 5 (5 self)
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In clique tree clustering, inference consists of propagation in a clique tree compiled from a Bayesian network. In this paper, we develop an analytical approach to characterizing clique tree growth as a function of increasing Bayesian network connectedness, speci cally: (i) the expected number
On finding minimumdiameter clique trees
 Nordic Journal of Computing
, 1991
"... Research was supported by the Applied Mathematical Sciences Research Program of the Office of Energy Reseaxch, U.S. Department of Energy. Prepared by the ..."
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Cited by 7 (1 self)
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Research was supported by the Applied Mathematical Sciences Research Program of the Office of Energy Reseaxch, U.S. Department of Energy. Prepared by the
Independence of Causal Influence and Clique Tree Propagation
 International Journal of Approximate Reasoning
, 1997
"... This paper explores the role of independence of causal influence (ICI) in Bayesian network inference. ICI allows one to factorize a conditional probability table into smaller pieces. We describe a method for exploiting the factorization in clique tree propagation (CTP)  the stateoftheart exact ..."
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Cited by 6 (0 self)
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This paper explores the role of independence of causal influence (ICI) in Bayesian network inference. ICI allows one to factorize a conditional probability table into smaller pieces. We describe a method for exploiting the factorization in clique tree propagation (CTP)  the state
Understanding the Scalability of Bayesian Network Inference using Clique Tree Growth Curves
"... Bayesian networks (BNs) are used to represent and ef ciently compute with multivariate probability distributions in a wide range of disciplines. One of the main approaches to perform computation in BNs is clique tree clustering and propagation. In this approach, BN computation consists of propagati ..."
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Cited by 4 (4 self)
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Bayesian networks (BNs) are used to represent and ef ciently compute with multivariate probability distributions in a wide range of disciplines. One of the main approaches to perform computation in BNs is clique tree clustering and propagation. In this approach, BN computation consists
Comparing Loop Cutsets and Clique Trees in Probabilistic Inference
, 1997
"... More and more knowledgebased systems are being developed that employ the framework of Bayesian belief networks for reasoning with uncertainty. Such systems generally use for probabilistic inference either the algorithm of J. Pearl or the algorithm of S.L. Lauritzen and D.J. Spiegelhalter. These alg ..."
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Cited by 4 (0 self)
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More and more knowledgebased systems are being developed that employ the framework of Bayesian belief networks for reasoning with uncertainty. Such systems generally use for probabilistic inference either the algorithm of J. Pearl or the algorithm of S.L. Lauritzen and D.J. Spiegelhalter. These algorithms build on different graphical structures for their underlying computational architecture. By comparing these structures we examine the complexity properties of the two algorithms and show that Lauritzen and Spiegelhalter's algorithm has at most the same computational complexity as Pearl's algorithm. 1 Introduction For reasoning with uncertainty in knowledgebased systems, the framework of Bayesian belief networks is rapidly gaining in popularity [Pea88]. The framework provides a powerful formalism for representing a joint probability distribution on a set of statistical variables and offers algorithms for probabilistic inference. Since its introduction in the late 1980s, the beliefn...
General Inference Algorithm of Bayesian Networks Based on Clique Tree
"... A general inference algorithm which based on exact algorithm of clique tree and importance sampling principle was put forward this article. It applied advantages of two algorithms, made information transfer from one clique to another, but don’t calculate exact interim result. It calculated and dealt ..."
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A general inference algorithm which based on exact algorithm of clique tree and importance sampling principle was put forward this article. It applied advantages of two algorithms, made information transfer from one clique to another, but don’t calculate exact interim result. It calculated
A Clique Tree Algorithm For Partitioning A Chordal Graph Into Transitive Subgraphs
, 1994
"... . A partitioning problem on chordal graphs that arises in the solution of sparse triangular systems of equations on parallel computers is considered. Roughly the problem is to partition a chordal graph G into the fewest transitively orientable subgraphs over all perfect elimination orderings of G, ..."
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Cited by 3 (2 self)
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. A more efficient greedy scheme, obtained by representing the chordal graph in terms of its maximal cliques, is described here. The new greedy scheme eliminates in a specified order a largest set of "persistent leaves", a subset of the leaf cliques in the current graph, at each step
Results 1  10
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26,438