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Engineering - Computational Intelligence and Complexity | Linear Models in the Mathematics of Uncertainty

Linear Models in the Mathematics of Uncertainty

Mordeson, J., Wierman, M., Clark, T.D., Pham, A., Redmond, M.A.

2013, XXVIII, 265 p. 14 illus.

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  • This books describes and discusses linear models in the mathematics of uncertainty including analytic hierarchy method, data fusion, and evidence theory
  • This book also provides some applications of these methods to a range of different practical situations
  • Written by leading experts in the field

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.

In this book, we examine cases where the number of data points is
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. 

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.

Content Level » Research

Keywords » Analytic Hierarchy Process - Data Fusion - Evidence Theory - Fuzzy Sets - Intuitionistic Fuzzy Sets - Mathematics of Uncertainty

Related subjects » Computational Intelligence and Complexity

Table of contents 

Part I Mathematics Of Uncertainty.- Part II The Problems.- Part III Applications.- Part IV Analysis of Results.

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