Degree Name

MS (Master of Science)

Program

Mathematical Sciences

Date of Award

5-2011

Committee Chair or Co-Chairs

Jeff R. Knisley

Committee Members

Ariel Cintron-Arias, Robert A. Beeler, Robert B. Gardner

Abstract

The probit function is the inverse of the cumulative distribution function associated with the standard normal distribution. It is of great utility in statistical modelling. The Carleman embedding technique has been shown to be effective in solving first order and, less efficiently, second order nonlinear differential equations. In this thesis, we show that solutions to the second order nonlinear differential equation for the probit function can be approximated efficiently using a variant of the Carleman embedding technique.

Document Type

Thesis - Open Access

Copyright

Copyright by the authors.

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