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

By Ankush Mittal, Ashraf Kassim

Bayesian networks at the moment are getting used in numerous man made intelligence functions. those networks are high-level representations of chance distributions over a suite of variables which are used for development a version of the matter area. Bayesian community applied sciences: purposes and Graphical types offers a very good and well-balanced number of components the place Bayesian networks were effectively utilized. This booklet describes the underlying options of Bayesian Networks in an engaging demeanour with the aid of various functions, and theories that end up Bayesian networks legitimate. Bayesian community applied sciences: functions and Graphical versions presents particular examples of ways Bayesian networks are strong desktop studying instruments severe in fixing real-life difficulties.

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The second step is to determine the relationships among the variables and establish the graphical structure of the model. The 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  third step, then, is to apply Bayesian rules to compute conditional probability values for each of variables in the model. The fourth stage of model building requires development of scenarios to update and train the model.

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.

For example, suppose the grass is wet, but that we also know that it is raining. Then the posterior probability that the sprinkler is on goes down. The inherent structure of a Bayesian model can be defined in terms of dependency and independency assumptions between variables, and it greatly simplifies the representation of the joint probability distribution capturing any dependencies, independences, conditional independences, and marginal independences between variables. A Bayesian model is usually composed of n variables and each variable is deliberately associated with those variables that lie under its influence.

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