Bayesian Network Technologies: Applications and Graphical by Ankush Mittal, Ashraf Kassim PDF


By Ankush Mittal, Ashraf Kassim

ISBN-10: 1599041413

ISBN-13: 9781599041414

ISBN-10: 159904143X

ISBN-13: 9781599041438

Bayesian networks are actually getting used in a number of synthetic intelligence functions. those networks are high-level representations of likelihood distributions over a collection of variables which are used for development a version of the matter area. Bayesian community applied sciences: functions and Graphical types offers an outstanding and well-balanced number of parts the place Bayesian networks were effectively utilized. This booklet describes the underlying innovations of Bayesian Networks in a fascinating demeanour with assistance from various functions, and theories that turn out Bayesian networks legitimate. Bayesian community applied sciences: purposes and Graphical versions offers particular examples of the way Bayesian networks are strong desktop studying instruments serious in fixing real-life difficulties.

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Extra info for Bayesian Network Technologies: Applications and Graphical Models

Sample text

The process of building models is an iterative one, involving organization of data, establishing logical relationships among the data, and coming up with a knowledge representation scheme. The process involves interaction of data, observation of a phenomenon, a knowledge representation scheme, and an emergent model (see Figure 1). A fundamental assumption underlying most of the model building process is that data is available in which a researcher can be able to infer logical relationships and draw logical and concrete conclusions from the model.

Higher Copyright © 2007, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. A Bayesian Belief Network Approach for Modeling Complex Domains  values of entropy reduction correspond to variables in strong paths, and it generally suggests that the qualitative reasoning used for deriving the initial probabilities presented in the social capital model is reasonable. Although results of the sensitivity analysis seem to suggest that different variables can affect social capital at different levels, at this point, further studies are required to determine the effects of individual variables on social capital.

Even though initial probabilities can be elicited from experts, it sometimes raises the problems of accuracy in values. Copyright © 2007, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. 4 Daniel, Zapata-Rivera, & McCalla In addition, translating experts’ qualitative knowledge into numerical probabilistic values is a daunting and often complex task. Because Bayesian network modeling involves establishing cause and effects among variables, it is sometimes difficult to determine causal relationships or to adequately describe all the causes and effects.

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Bayesian Network Technologies: Applications and Graphical Models by Ankush Mittal, Ashraf Kassim

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