Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. They are becoming an increasingly important area fог research and applications in the entire field of Artificial Intelligence. This paper explores the nature of implications for Bayesian networks beginning with an overview and comparison of inferential statistics and Bayes’ Theorem. It presents the possibilities of applications of Bayesian networks in a field of economic problems and also focuses on the problem of learning.