Linear Models in the Mathematics of Uncertainty
By: by John N. Mordeson, Mark J. Wierman, Terry D. Clark, Alex Pham, Michael A. Redmond
Contributors: Wierman, Mark J.; Clark, Terry D.; Pham, Alex; Redmond, Michael A.; SpringerLink (Online service)
Series: Studies in Computational Intelligence -- 463
Publisher: Springer Berlin Heidelberg; Imprint: Springer
Published: 2013
Place of publication: Berlin, Heidelberg
Language: eng
Format: [electronic resource] /
Number of pages: 265
ISBN-13: 9783642352249
Subjects: Engineering; Computational intelligence
Description: <p>The purpose of this book is to present new mathematical techniques for modeling global issues. These mathematical techniques are used to determine linear equations between a dependent variable and one or more independent variables in cases where standard techniques such as linear regression are not suitable. </p><p>In this book, we examine cases where the number of data points is<br>small (effects of nuclear warfare), where the experiment is not repeatable (the breakup of the former Soviet Union), and where the data is derived from expert opinion (how conservative is a political party). In all these cases the data  is difficult to measure and an assumption of randomness and/or statistical validity is questionable.  </p><p>We apply our methods to real world issues in international relations such as  nuclear deterrence, smart power, and cooperative threat reduction. We next apply our methods to issues in comparative politics such as successful democratization, quality of life, economic freedom, political stability, and failed states. Finally, issues involving deaf and hard of hearing children are explored. <br> </p>
Table of contents: title: <p>Part I Mathematics Of Uncertainty; title: Part II The Problems; title: Part III Applications; title: Part IV Analysis of Results.</p><p><p><p><p>.