A Comparison of Bayesian Regression Models Applied in Knot Theory

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.

Name thesis:degree_name

MS

Level thesis:degree_level

Immediate Access

Discipline thesis:degree_discipline

Computational Mathematics

Year dc:date.available

2008

Author dc:creator

• Bilir, Sevcan

Contributors dc:contributor

• John C. Kern
• Mark Mazur
• Donald Simon

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

Language dc:language

English

Repository record dc:identifier

https://dsc.duq.edu/etd/318

OAI identifier oai:identifier

oai:dsc.duq.edu:etd-1331