Towards effective improvement of the Bayesian Belief Network Structure learning

Abstract

Summary form only given.The Bayesian Belief Network (BBN) is a very powerful tool for causal relationship modeling and probabilistic reasoning. A BBN has two components. First is its structure a directed acyclic graph (DAG) whose nodes represent random variables and whose arcs represent the dependencies between the variables. The second component is its parameter in the form of Conditional Probability Tables (CPTs).The BBN is widely used in many different areas, excelling itself in Prediction, Risk Analysis, Diagnosis and Decision Support.