Abstract

: 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. Keywords: Decomposition, uncertain reasoning, probabilistic networks, possibilistic networks, learning from data. 1. Introduction Since reasoning in multi-dimensional domains tends to be infeasible in the domains as a whole---and the more so, if uncertainty and/or imprecision are involved---decomposition techniques, that reduce the reasoning process to computations in low...