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Structural Learning of Dynamic Bayesian Networks in Speech Recognition
, 2001
"... this paper, X i denotes a continuous or discrete random variable. Values of the random variable will be indicated by lower case letters as in x i . For a discrete variable that takes r values, x i denote a speci c assignment for 1 k r. A set of variables is denoted in boldface letters X = fX 1 ; ..."
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Cited by 8 (4 self)
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this paper, X i denotes a continuous or discrete random variable. Values of the random variable will be indicated by lower case letters as in x i . For a discrete variable that takes r values, x i denote a speci c assignment for 1 k r. A set of variables is denoted in boldface letters X = fX 1 ; : : : ; Xn g
Fast Parallel Algorithms for the Clique Separator Decomposition
, 1990
"... We give an efficient NC algorithm for finding a clique separator decomposition of an arbitrary graph, that is, a series of cliques whose removal disconnects the graph. This algorithm allows one to extend a large body of results which were originally formulated for chordal graphs to other classes of ..."
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Cited by 5 (1 self)
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We give an efficient NC algorithm for finding a clique separator decomposition of an arbitrary graph, that is, a series of cliques whose removal disconnects the graph. This algorithm allows one to extend a large body of results which were originally formulated for chordal graphs to other classes of graphs. Our algorithm is optimal to within a polylogarithmic factor of Tarjan's O(mn) time sequential algorithm. The decomposition can also be used to find NC algorithms for some optimization problems on special families of graphs, assuming these problems can be solved in NC for the prime graphs of the decomposition. These optimization problems include: finding a maximumweight clique, a minimum coloring, a maximumweight independent set, and a minimum fillin elimination order. We also give the first parallel algorithms for solving these problems by using the clique separator decomposition. Our maximumweight independent set algorithm applied to chordal graphs yields the most efficient know...
A join tree probability propagation architecture for semantic modeling
 J INTELL INF SYST
, 2008
"... ..."
An Improved LAZYAR Approach to Bayesian Network Inference
"... Abstract. We propose LAZY arcreversal with variable elimination (LAZYARVE) as a new approach to probabilistic inference in Bayesian networks (BNs). LAZYARVE is an improvement upon LAZY arcreversal (LAZYAR), which was very recently proposed and empirically shown to be the stateoftheart method ..."
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Cited by 3 (2 self)
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Abstract. We propose LAZY arcreversal with variable elimination (LAZYARVE) as a new approach to probabilistic inference in Bayesian networks (BNs). LAZYARVE is an improvement upon LAZY arcreversal (LAZYAR), which was very recently proposed and empirically shown to be the stateoftheart method for exact inference in discrete BNs. The primary advantage of LAZYARVE over LAZYAR is that the former only computes the actual distributions passed during inference, whereas the latter may perform unnecessary computation by constructing irrelevant intermediate distributions. A comparison between LAZYAR and LAZYARVE, involving processing evidence in a realworld BN for coronary heart disease, is favourable towards LAZYARVE. 1
HIERARCHICAL PREDICTION OF PROTEIN FUNCTION IN THE GENE ONTOLOGY USING GRAPHICAL MODELS
"... copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein before provided, neither the thesis nor any substantial ..."
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copies of this thesis and to lend or sell such copies for private, scholarly or scientific research purposes only. The author reserves all other publication and other rights in association with the copyright in the thesis, and except as herein before provided, neither the thesis nor any substantial portion thereof may be printed or otherwise reproduced in any material form whatever without the author’s prior written permission.
HUGIN* — a Shell for Building Bayesian Belief Universes for
"... Causal probabilistic networks have proved to be a useful knowledge representation tool for modelling domains where causal relations in a broad sense are a natural way of relating domain objects and where uncertainty is inherited in these relations. This paper outlines an implementation the HUGIN she ..."
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Causal probabilistic networks have proved to be a useful knowledge representation tool for modelling domains where causal relations in a broad sense are a natural way of relating domain objects and where uncertainty is inherited in these relations. This paper outlines an implementation the HUGIN shell for handling a domain model expressed by a causal probabilistic network. The only topological restriction imposed on the network is that, it must not contain any directed loops. The approach is illustrated step by step by solving a. genetic breeding problem. A graph representation of the domain model is interactively created by using instances of the basic network components— nodes and arcs—as building blocks. This structure, together with the quantitative relations between nodes and their immediate causes expressed as conditional probabilities, are automatically transformed into a tree structure, a junction tree. Here a computationally efficient and conceptually simple algebra of Bayesian belief universes supports incorporation of new evidence, propagation of information, and calculation of revised beliefs in the states of the nodes in the network. Finally, as an exam ple of a real world application, MUN1N an expert system for electromyography is discussed. 1
GRID*p: Interactive DataParallel Programming on the Grid with MATLAB
"... Abstract — The Computational Grid has emerged as an attractive platform for developing largescale distributed applications that run on heterogeneous computing resources. This scalability, however, comes at the cost of increased complexity: each application has to handle the details of resource prov ..."
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Abstract — The Computational Grid has emerged as an attractive platform for developing largescale distributed applications that run on heterogeneous computing resources. This scalability, however, comes at the cost of increased complexity: each application has to handle the details of resource provisioning and data and task scheduling. To address this problem, we present GRID*p an interactive parallel system built on top of MATLAB*p and Globus that provides a MATLABlike problem solving environment for the Grid. Applications under GRID*p achieve automatic data parallelism through the transparent use of distributed data objects, while the GRID*p runtime takes care of data partitioning, task scheduling and internode messaging. We evaluate the simplicity and performance of GRID*p with two different types of parallel applications consisting of matrix and graph computations one of which investigates an open problem in combinatorial scientific computing. Our results show that GRID*p delivers promising performance for highly parallel applications, at the same time greatly simplifying their development. I.