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The goal of the paper is to introduce a program system, MUDIM,and to show how it can be used for multidimensional probabilisticmodel construction. The system is being developed with the goalto gain a tool for experimental computations with compositionalmodels which are, in a way, an alternative to Bayesian networks.These models are based on the idea of composing a multidimensionaldistribution from a great number of low-dimensional ones. Whenconsidering knowledge-based systems, this approach quite naturallycope with the difficulty of expressing global knowledge abouta field of practise. We have only to work with a system of piecesof local knowledge from which the global knowledge must be assembled. 相似文献
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