Seminar@SystemX - 19 janvier - Philippe Leray - Advances in Learning with Bayesian Networks
Bayesian networks (BNs) are a powerful tool for graphical representation of the underlying knowledge in the data and reasoning with incomplete or imprecise observations. BNs have been extended (or generalized) in several ways, as for instance, causal BNs, dynamic BNs, Relational BNs, … In this talk, we will focus on Bayesian network learning. BN learning can differ with respect to the task : generative model versus discriminative one ? Then, the learning task can also differ w.r.t the nature of the data : complete data, incomplete data, non i.i.d data, number of variables number of samples, data stream, presence of prior knowledge …Given the diversity of these problems, many approaches have emerged in the literature. I will present a brief panorama of those algorithms and describe our current works in this field.