Using Dynamic and Hybrid Bayesian Network for Policy Decision Making

Using Dynamic and Hybrid Bayesian Network for Policy Decision Making

Tabassom Sedighi (Cranfield University, Cranfield, UK)
Copyright: © 2019 |Pages: 13
DOI: 10.4018/IJoSE.2019070103
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The Bayesian network (BN) method is one of the data-driven methods which have been successfully used to assist problem-solving in a wide range of disciplines including policy making, information technology, engineering, medicine, and more recently biology and ecology. BNs are particularly useful for diverse problems of varying size and complexity, where uncertainties are inherent in the system. BNs engage directly with subjective data in a transparent way and have become a state-of-the-art technology to support decision-making under uncertainty.
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Bayesian Network

A BN is a probabilistic graphical model which is used to represent knowledge about an uncertain domain. Each random variable (a variable whose possible values are outcomes of a random phenomenon) is presented by a node in the BN and to each node, there is attached a conditional probability table. In general, three classes of nodes exist in BN: (i) nodes without a child node are called leaf nodes, (ii) nodes without a parent node are called root nodes, and (iii) nodes with parent and child nodes are called intermediate nodes. The directed links (edges) between the nodes represent probabilistic dependencies among these nodes. The direct casual relation between two nodes shows that the corresponding nodes will have a greater influence on the system than others. The only constraint on the links allowed in a BN is that there must not be any directed cycles: one cannot return to a node simply by following a series of directed links.

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