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Using Path Diagrams as a Structural Equation Modelling Tool
, 1997
"... this paper, we will show how path diagrams can be used to solve a number of important problems in structural equation modelling. There are a number of problems associated with structural equation modeling. These problems include: ..."
Abstract

Cited by 29 (7 self)
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this paper, we will show how path diagrams can be used to solve a number of important problems in structural equation modelling. There are a number of problems associated with structural equation modeling. These problems include:
Information Fusion, Causal Probabilistic Network And Probanet II: Inference Algorithms and Probanet System
 Proc. 1st Intl. Workshop on Image Analysis and Information Fusion
, 1997
"... As an extension of an overview paper [Pan and McMichael, 1997] on information fusion and Causal Probabilistic Networks (CPN), this paper formalizes kernel algorithms for probabilistic inferences upon CPNs. Information fusion is realized through updating joint probabilities of the variables upon the ..."
Abstract

Cited by 2 (2 self)
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As an extension of an overview paper [Pan and McMichael, 1997] on information fusion and Causal Probabilistic Networks (CPN), this paper formalizes kernel algorithms for probabilistic inferences upon CPNs. Information fusion is realized through updating joint probabilities of the variables upon the arrival of new evidences or new hypotheses. Kernel algorithms for some dominant methods of inferences are formalized from discontiguous, mathematicsoriented literatures, with gaps lled in with regards to computability and completeness. In particular, possible optimizations on causal tree algorithm, graph triangulation and junction tree algorithm are discussed. Probanet has been designed and developed as a generic shell, or say, mother system for CPN construction and application. The design aspects and current status of Probanet are described. A few directions for research and system development are pointed out, including hierarchical structuring of network, structure decomposition and adaptive inference algorithms. This paper thus has a nature of integration including literature review, algorithm formalization and future perspective.