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. / McAnulty College and Graduate School of Liberal Arts; / Computational Mathematics / MS; / Thesis;
| Identifer | oai:union.ndltd.org:DUQUESNE/oai:digital.library.duq.edu:etd/156427 |
| Date | 08 August 2012 |
| Creators | Maldonado, Hernan |
| Contributors | John Kern, Eric Ruggieri, Donald Simon |
| Source Sets | Duquesne University |
| Detected Language | English |
| Rights | Worldwide Access; |
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