It is well known that among all probabilistic graphical Markov models the class of decomposable models is the most advan- tageous in the sense that the respective distributions can be expressed with the help of their marginals and that the most efficient computa- tional procedures are designed for their processing (for example profes- sional software does not perform computations with Bayesian networks but with decomposable models into which the original Bayesian network is transformed). This paper introduces a definition of the counterpart of these models within Dempster-Shafer theory of evidence, makes a survey of their most important properties and illustrates their efficiency on the problem of approximation of a "sample distribution" for a data file with missing values.
['Radim Jiroušek']
Approximation of Data by Decomposable Belief Models