Study of the Bayesian networks

A Bayesian network is a graphical model that finds probabilistic relationships among rubles of the system. Bayesian networks pass evidence (data) between nodes and use the expectations from the world model, they can be considered as bi-directional learning systems. In this paper, we provide a detailed definition of Bayesian networks and related theorems. The chain rule theorem is introduced to do the necessary calculations in Bayesian networks. We provide theoretical and historical details on evidential reasoning using the chain rule. Then we explore some questions about the relationship between Bayesian Networks and the functionality of a human brain as our last topic in Bayesian networks. Finally, we introduce influence diagrams method to convert beliefs of an agent into actions.