First Advisor

Steven A. Bleiler

Date of Publication

1-1-2011

Document Type

Dissertation

Degree Name

Doctor of Philosophy (Ph.D.) in Mathematical Sciences

Department

Mathematics and Statistics

Language

English

Subjects

Random variables -- Mathematical models, Programming (Mathematics), Mathematical models

DOI

10.15760/etd.282

Physical Description

1 online resource (xi, 267 p.)

Abstract

This dissertation is motivated by the problem of uncertainty and sensitivity in business- class models such as the carbon emission abatement policy model featured in this work. Uncertain model inputs are represented by numerical random variables and a computational methodology is developed to numerically compute business-class models as if sharp inputs were given. A new description for correlation of random variables is presented that arises spontaneously within a numerical model. Methods of numerically computing correlated random variables are implemented in software and represented. The major contribution of this work is a methodology for the numerical computation of models under uncertainty that expresses no preference for unlikelihood of model input combinations. The methodology presented here serves a sharp contrast to traditional Monte Carlo methods that implicitly equate likelihood of model input values with importance of results. The new methodology herein shifts the computational burden from likelihood of inputs to resolution of input space.

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Comments

Portland State University. Dept. of Mathematics and Statistics

Persistent Identifier

http://archives.pdx.edu/ds/psu/7052

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