Bayesian Predictive Density Estimation with Additional Information

In the context of Bayesian theory and decision theory, the estimation of a predictive density of a random variable represents an important and challenging problem. Often the times there is some additional information at our disposal which is unduly being ignored. In this talk, we deal with strategies to take into account this kind of information, in order to obtain effective and sometimes better performing predictive densities than others in the literature.