Interactive learning of Bayesian Networks using OpenMarkov
Iñigo Bermejo, Jesús Oliva, Francisco Javier D ́ iez, Manuel Arias · 2012
Algorithms for learning Bayesian networks (BNs) behave as a black box that takes a database as an input and returns a network as the output. In contrast, OpenMarkov, our tool for probabilistic graphical models, includes the option to run the algorithms in a step-by-step fashion, presenting a ranked list of operations (such as adding, removing, or inverting links) the user can select, while allowing live edition of the BN throughout the learning process. The application oers some data preprocessing options and the possibility to use a model network to guide the learning process. This functionality in OpenMarkov can be employed to learn BNs with partial expert knowledge, to debug new algorithms, and as a pedagogical tool.