Implicit parameter estimation for conditional Gaussian Bayesian networks

The Bayesian estimation of the conditional Gaussian parameter needs to define several a priori parameters. The proposed approach is free from this definition of priors. We use the Implicit estimation method for learning from observations without a prior knowledge. We illustrate the interest of such an estimation method by giving first the Bayesian Expectation A Posteriori estimator for conditional Gaussian parameters. Then, we describe the Implicit estimators for the same parameters. Moreover, an experimental study is proposed in order to compare both approaches.