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

By Ankush Mittal, Ashraf Kassim

ISBN-10: 1599041413

ISBN-13: 9781599041414

Bayesian networks at the moment are getting used in various man made 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 versions presents a superb and well-balanced number of components the place Bayesian networks were effectively utilized. This e-book describes the underlying strategies of Bayesian Networks in an attractive demeanour with assistance from assorted purposes, and theories that turn out Bayesian networks legitimate. Bayesian community applied sciences: functions and Graphical versions presents particular examples of the way Bayesian networks are strong computer studying instruments serious in fixing real-life difficulties.

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In Proceedings of the Workshop on Intelligent Data Analysis in Medicine and Pharmacology (IDAMAP-2002), Lyon, France. , Danis, C. Miller, T. & Jung, Y. (2001). Fostering social interaction in online spaces. In M. 13 Conference on HumanComputer Interaction (INTERACT’01) (pp. 59-66). Amsterdam: IOS Press. McCalla, G. (2000). The fragmentation of culture, learning, teaching and technology: Implications for artificial intelligence in education research. International Journal of Artificial Intelligence, 11(2), 177-196.

Copyright © 2007, IGI Global. Copying or distributing in print or electronic forms without written permission of IGI Global is prohibited. , & Herskovits, E. (1992). A Bayesian method for the induction of probabilistic networks from data. Machine Learning, 9(4), 309-347. , & VanLehn, K. (2002). Using bayesian networks to manage uncertainty in student modeling. User Modeling and User-Adapted Interaction, 12(4), 371-417. C. (1998). Practicable sensitivity analysis of Bayesian belief networks. In M.

For instance, in virtual learning communities, people’s attitudes can strongly influence the level of their awareness on various issues, which in turn can influence trust. 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 2 Table 1. Social capital variables and their definitions (Daniel, McCalla, & Schwier, 2005) Variable Name Variable Definition Variable States Interaction A mutual or reciprocal action between two or more agents determined by the number of messages sent and received Positive/Negative Attitudes Individuals’ general perception about each other and others’ actions Positive/Negative Community Type The type of environment, tools, goals, and tasks that define the group Virtual learning community (VLC) and Distributed community of practice (DCoP) Shared Understanding A mutual agreement/consensus between two or more agents about the meaning of an object or idea High/Low Awareness Knowledge of people, tasks, or environment, or all of the above Present/Absent Demographic Awareness Knowledge of an individual: country of origin, language, and location Present/Absent Professional Awareness Knowledge of people’s background training, affiliation, and so forth Present/Absent Competence Awareness Knowledge about an individual’s capabilities, competencies, and skills Present/Absent Capability Awareness Knowledge of people’s competences and skills in regard to performing a particular task Present/Absent Social protocols The mutually agreed upon, acceptable and unacceptable ways of behaviour in a community Present/Absent Trust A particular level of certainty or confidence with which an agent uses to assess the action of another agent.

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