Abstract
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. © 2010 IEEE.
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CITATION STYLE
Tang, Y., Cooper, K., Cangussu, J., Tian, K., & Wu, Y. (2010). Towards effective improvement of the Bayesian belief network structure learning. In ISI 2010 - 2010 IEEE International Conference on Intelligence and Security Informatics: Public Safety and Security (p. 174). https://doi.org/10.1109/ISI.2010.5484745
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