Oregon State University

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MS Final Examination – Juliana Mbuthia


Wednesday, June 4, 2014 12:00 PM - 2:00 PM

Parameter Estimation of Gaussian Hierarchical Model using Gibbs Sampling
Gibbs sampling method is an important tool used in parameter estimation for many probabilistic models. Specifically, for many scenarios, it is difficult to generate high- dimensional data samples from its joint distribution. The Gibbs sampling provides a way to draw high-dimensional data via the conditional distributions which are typically easier to sample. In this thesis, we study a simple generative model called Hierarchical Gaussian and an efficient method for computing its parameters using Gibbs sampling. In particular, we show that the Hierarchical Gaussian model admits closed form full conditional distributions such that Gibbs sampling can be used effectively to draw the samples from the joint distribution, and perform parameter estimation.

Major Advisor: Thinh Nguyen
Minor Advisor: Charlotte Wickham
Committee: Raviv Raich
GCR: Margaret Niess 


Kelley Engineering Center (campus map)
1007
Nicole Thompson
1 541 737 3617
Nicole.Thompson at oregonstate.edu
Sch Elect Engr/Comp Sci
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