Definition: In this task, you are given a part of an article. Your task is to generate headline (title) for this text. Preferred headlines are under fifteen words.
Input: Conditional independence and Markov prop­ erties are powerful tools allowing expression of multidimensional probability distributions by means of low-dimensional ones. As mul­ tidimensional possibilistic models have been studied for several years, the demand for analogous tools in possibility theory seems to be quite natural. This paper is intended to be a promotion of de Cooman's measure­ theoretic approach to possibility theory, as this approach allows us to find analogies to many important results obtained in prob­ abilistic framework. First we recall semi­ graphoid properties of conditional possibilis­ tic independence, parameterized by a contin­ uous t-norm, and find sufficient conditions for a class of Archimedean t-norms to have the graphoid property. Then we introduce Markov properties and factorization of possi­ bility distributions (again parameterized by a continuous t-norm) and find the relation­ ships between them. These results are ac­ companied by a number of counterexamples, which show that the assumptions of specific theorems are substantial.

Conditional Independence and Markov Properties in Possibility Theory