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

ornithology

An Introduction to Nested SamplingIn particular, enumerating some of the distinctions that can be supported by a qualitative probability language induces a partial taxonomy of possible approaches. I discuss some of these in further depth, identify central issues, and suggest some general comparisons.

有花

FLING

The Strategy of Nested Samplings combination, marginalization, conditioning and integration are considered for probability intervals. Moreover, the relationships of this theory with some others, as lower and upper probabilities and Choquet capacities of order two are also clarified. The advantages of probability intervals with re

聽覺(jué)

An Introduction to Nested Samplingl probability bounds defined on . is considered. Following the approach of de Finetti we define the concept of coherence for the given set of bounds. Then, some results on the probabilistic consistency of the knowledge base are obtained. Finally, an algorithm to check the coherence of the set of bou

尊敬

Lecture Notes in Computer Sciencehttp://image.papertrans.cn/a/image/148485.jpg

Dedication

B-cell

https://doi.org/10.1007/978-3-030-02459-8We introduce a methodology for performing approximate computations in complex probabilistic expert systems, when some components can be handled exactly and others require approximation or simulation. This is illustrated by means of a modified version of the familiar ‘chest-clinic’ problem.

膽小鬼

Classical Statistical MechanicsIn this paper, decision influence diagrams are studied when the assessment of utilities with real numerical values is considered to be too restrictive, and the use of fuzzy sets to model the problem in terms of fuzzy utilities seems appropiate. An algorithm to solve decision influence diagrams with fuzzy utilities is suggested.

Malaise

The Lagrangian-Hamiltonian Theory,usibility functions on hypotheses, which constitute the numerical part of evidence theory. Combination of evidence based on the combination of bodies of arguments is discussed and Dempster‘s rule is derived.

發(fā)展

Distribution Functions of Dynamic Systemsed method allows us to change the structure of the Markov tree without changing any information contained in the nodes, thus avoids the possible repeated computations. Moreover, it can compute the marginal on any subset from the marginal representation already obtained. An efficient implementation of this method is also proposed.