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Titlebook: Advances in Intelligent Computing - IPMU ‘94; 5th International Co Bernadette Bouchon-Meunier,Ronald R. Yager,Lotfi A Conference proceeding

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Fundamental Theories of Physicsd probability and non probability models, according to the fact they are based on probability theory or not. The first group concerns the upper and lower probability models, the second the possibility theory and the transferable belief model.
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The Lagrangian-Hamiltonian Theory,n allocation of support and an allowment of possibility for each hypothesis. A rule of combination of bodies of arguments is defined which constitutes the symbolic counterpart of Dempster‘s rule. Bodies of evidence are introduced by assigning probabilities to arguments. This leads to support and pla
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Introduction to Electrical Relaxationtegral, belief measures can be extended to be coherent lower previsions on the linear space consisting of all bounded functions. As a special case, we establish that all belief measures are coherent imprecise probabilities.
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https://doi.org/10.1007/978-94-015-8963-5btained from different triangulations of the original graph. The complexity of the resulting propagation algorithms depends on the size of the resulting triangulated graph. The problem of obtaining an optimum graph triangulation is known to be NP-complete. Thus approximate algorithms which find a .
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Distribution Functions of Dynamic Systemsary structures called clique trees or Markov trees. However, these techniques only compute the marginal on the subset of variables contained in one node of the secondary structure. This paper presents a method for computing the marginal on the subset that may not be contained in one node. The propos
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https://doi.org/10.1007/978-94-015-8963-5is that it takes full advantage of the capabilities of conventional relational database systems for probabilistic inference. Belief update, for example, can be processed as an ordinary query, and the techniques for query optimization are directly applicable to updating beliefs. The results of this p
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Statistical Mechanics of Liquids engineering point of view, we present causal networks, introduce a concept of network parameters, propose some principles for construction and use of causal networks, and indicate the advantages of the knowledge bases with the form of causal networks. In order to put them into practice and incorpor
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https://doi.org/10.1007/978-3-319-66769-0rimentation. Evidence propagation operations are critical for the computation of the value of evidence, general update and inference operations in normative expert systems which are based on the influence diagram (Bayesian Network) paradigm. The value of evidence allows us to compute directly a valu
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