Interactive learning of Bayesian Networks using OpenMarkov

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. Database preprocessing options allow the user to select the variables to be used, indicate how to discretize numeric variables and impute missing data. It is also possible to use a model network to guide the learning process in different ways. This functionality in OpenMarkov can be employed to learn BNs with partial expert knowledge, to debug new algorithms, and as a pedagogical tool.