Bayesian Regression Inference Using a Normal Mixture Model

In this thesis we develop a two component mixture model to perform a Bayesian regression. We implement our model computationally using the Gibbs sampler algorithm and apply it to a dataset of differences in time measurement between two clocks. The dataset has ``good" time measurements and ``bad" time measurements that were associated with the two components of our mixture model. From our theoretical work we show that latent variables are a useful tool to implement our Bayesian normal mixture model with two components. After applying our model to the data we found that the model reasonably assigned probabilities of occurrence to the two states of the phenomenon of study; it also identified two processes with the same slope, different intercepts and different variances.

Name thesis:degree_name

MS

Level thesis:degree_level

Immediate Access

Discipline thesis:degree_discipline

Computational Mathematics

Year dc:date.available

2012

Author dc:creator

• Maldonado, Hernan

Contributors dc:contributor

• John Kern
• Eric Ruggieri
• Donald Simon

• Bayesian regression
• Gibbs sampler
• Label switching problem
• Latent variables
• Mixture models
• Two component

Language dc:language

English

Repository record dc:identifier

https://dsc.duq.edu/etd/859

OAI identifier oai:identifier

oai:dsc.duq.edu:etd-1875