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Please use this identifier to cite or link to this item: http://hdl.handle.net/11375/17589
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dc.contributor.advisorSiddall, J. N.-
dc.contributor.authorDiab, Yosri-
dc.date.accessioned2015-06-22T19:33:42Z-
dc.date.available2015-06-22T19:33:42Z-
dc.date.issued1972-04-
dc.identifier.urihttp://hdl.handle.net/11375/17589-
dc.description.abstractThis thesis introduces a new effective method in statistical modeling and probabilistic decision making problems. The method is based on maximizing the Shannon Logarithmic Entropy Function for information, subject to the given prior information to serve as constraints, to generate a probability distribution. The method is known as the Maximum Entropy Principle or "Jaynes Principle". Tribus used it earlier, but in a limited case, without general application to either statistical modeling or probablistic decision making. In this thesis, a new method which generalizes the above principle is introduced. This permits practical applications, some of which are illustrated.en_US
dc.language.isoenen_US
dc.subjectmechanical engineeringen_US
dc.subjectmaximizationen_US
dc.subjectlogarithmic entropy functionen_US
dc.subjectstatistical modelingen_US
dc.subjectanalytical decision makingen_US
dc.titleThe Maximization of the Logarithmic Entropy Function as a New Effective Tool in Statistical Modeling and Analytical Decision Makingen_US
dc.contributor.departmentMechanical Engineeringen_US
dc.description.degreetypeThesisen_US
dc.description.degreeMaster of Engineering (ME)en_US
Appears in Collections:Open Access Dissertations and Theses

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