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http://hdl.handle.net/11375/17589
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DC Field | Value | Language |
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dc.contributor.advisor | Siddall, J. N. | - |
dc.contributor.author | Diab, Yosri | - |
dc.date.accessioned | 2015-06-22T19:33:42Z | - |
dc.date.available | 2015-06-22T19:33:42Z | - |
dc.date.issued | 1972-04 | - |
dc.identifier.uri | http://hdl.handle.net/11375/17589 | - |
dc.description.abstract | This 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.iso | en | en_US |
dc.subject | mechanical engineering | en_US |
dc.subject | maximization | en_US |
dc.subject | logarithmic entropy function | en_US |
dc.subject | statistical modeling | en_US |
dc.subject | analytical decision making | en_US |
dc.title | The Maximization of the Logarithmic Entropy Function as a New Effective Tool in Statistical Modeling and Analytical Decision Making | en_US |
dc.contributor.department | Mechanical Engineering | en_US |
dc.description.degreetype | Thesis | en_US |
dc.description.degree | Master of Engineering (ME) | en_US |
Appears in Collections: | Open Access Dissertations and Theses |
Files in This Item:
File | Description | Size | Format | |
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Diab_Yosri_1972April_MEng.pdf | 37.67 MB | Adobe PDF | View/Open |
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