The aim of this work is to improve the results obtained in the literature regarding informing probability distributions in the field of financial markets. The main idea is related to Information Theory, as the goal is to inform the prior distribution with views or additional information external to the owned data, to obtain the posterior distribution modifying the prior in an optimal and meaningful view-based way. Specifically, being a Bayesian approach, the final goal for these frameworks is to inform historical data with experts’ or proprietary views, based on the concept of minimizing the divergence between the prior and the posterior distributions, namely, avoiding to introduce unknown information.