The paper investigates the construction of a joint graph as global structure network based on its primary structure, one problems arising in machine learning bases knowledge patterns with uncertainty, presented form algebraic Bayesian networks. aim research is to propose methods for solving inverse problem. As results, algorithms checking belonging family graphs and minimal are proposed, estima...

In this paper we present an algorithm and software for generating arbitrarily large Bayesian Networks by tiling smaller real-world known networks. The algorithm preserves the structural and probabilistic properties of the tiles so that the distribution of the resulting tiled network resembles the realworld distribution of the original tiles. By generating networks of various sizes one can study...