Learning a Bayesian network consists in estimating the graph (structure) and the parameters of conditional probability distributions associated with this graph. Bayesian networks learning algorithms rely on classical Bayesian estimation approach whose a priori parameters are often determined by an expert or defined uniformly The core of this work concerns the application of several advances in the field of statistics as implicit estimation, Natural exponential families or infinite mixtures of Gaussian in order to (1) provide new parametric forms for Bayesian networks, (2) estimate the parameters of such models and (3) learn their structure.
