Author

Sevcan Bilir

Defense Date

4-17-2008

Graduation Date

2008

Availability

Immediate Access

Submission Type

thesis

Degree Name

MS

Department

Computational Mathematics

School

McAnulty College and Graduate School of Liberal Arts

Committee Chair

John C. Kern

Committee Member

Mark Mazur

Committee Member

Donald Simon

Keywords

Posterior distribution, prior distribution, mcmc sampling, gibbs sampling, credibility interval

Abstract

This thesis explores variations on a Bayesian regression model used to estimate the mean box length of a random knot as a function of the number of edges of that knot. Specifically, this research recognizes uncertainty in box length variance and compares the resulting inference with that based on an approach that does not recognize such uncertainty. The Bayesian model is then shown to allow straightforward inference on the crossing location of two population regression lines.

Format

PDF

Language

English

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