Defense Date
4-17-2008
Graduation Date
Summer 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
Language
English
Recommended Citation
Bilir, S. (2008). A Comparison of Bayesian Regression Models Applied in Knot Theory (Master's thesis, Duquesne University). Retrieved from https://dsc.duq.edu/etd/318