Possibilistic Belief Network Constructed by Operators of Composition and its Application to Financial Analysis
Bayesian networks have become one of the most popular multidimensional models for uncertain knowledge formalization. Possibilistic belief networks that are their possibilistic counterpart, though not so popular, seem to possess a couple of minor but pleasant advantages: the distributions are easier to modify to adopt expert subjective knowledge, and conditioning in possibility theory is dependent on a selected t-norm and therefore the apparatus seems to be more flexible.
The main part of this paper describes a new way of defining possibilistic belief networks as a sequence of low-dimensional distributions connected by operators of composition. To make this nonstandard technique more easily comprehensible, we introduce an extended motivation part explaining the basic notions through a simple (probabilistic) example. The paper is concluded by a lucid example: an application of the apparatus for fundamental analysis of engineering enterprises.
KeywordsBayesian Network Acyclic Directed Graph Debt Ratio Multidimensional Model Possibility Distribution
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