Probabilistic and Possibilistic Networks and How To Learn Them from Data
نویسندگان
چکیده
In this paper we explain in a tutorial manner the technique of reasoning in probabilistic and possibilistic network structures, which is based on the idea to decompose a multi-dimensional probability or possibility distribution and to draw inferences using only the parts of the decomposition. Since constructing probabilistic and possibilistic networks by hand can be tedious and time-consuming, we also discuss how to learn probabilistic and possibilistic networks from a data, i.e. how to determine from a database of sample cases an appropriate decomposition of the underlying probability or possibility distribution.
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