The hugin tool for learning bayesian networks

Abstract

In this paper, we describe the Hugin Tool as an efficient tool for knowledge discovery through construction of Bayesian networks by fusion of data and domain expert knowledge. The Hugin Tool supports structural learning, parameter estimation, and adaptation of parameters in Bayesian networks. The performance of the Hugin Tool is illustrated using real-world Bayesian networks, commonly used examples from the literature, and randomly generated Bayesian networks.